JP2006252107A - Image processing device, image processing method, and its program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image processing device capable of quickly obtaining a high quality enlarged image, with a comparatively light processing load. <P>SOLUTION: In the image processing device 2, with regard to a spotlighted pixel included in an input image, an image area of a predetermined size, included in the spotlighted pixel is distinguished, on the basis of the characteristics of the image area, the feature amount of the distinguished image area is calculated, one enlarging technique from a plurality of enlarging techniques is applied, on the basis of the calculated feature amount, with regard to the distinguished image area, an enlarge image area corresponding to the image area is generated, and the generated enlarged image area is arranged in an output image. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to an image processing apparatus that enlarges an image.

The image enlargement process is one of the basic processes for a system that performs image editing, filing, display, printing, and the like. In recent years, with the widespread use of image data mainly for display at display resolutions such as images on the Internet homepage and digital video, these low-resolution images are frequently printed by high-resolution printers. It has been broken. At the time of printing with this printer, it is desired to obtain a high-quality output result, and the importance of the high-quality enlargement process is increasing.
In addition, image data output by each application is diversified as hardware such as PCs, workstations, displays or printers, and software used in the hardware increase in functionality.

  FIG. 1 is a diagram illustrating the configuration of image data. FIG. 1A illustrates a source image, FIG. 3B illustrates a mask image that is a binary image, and FIG. FIG. 1D shows image data after an image that has been masked using the source image and the mask image is pasted into the background image. When outputting image data of a plurality of configurations with a high-resolution printer or the like, a method of enlarging the image data after configuration (FIG. 1D) is also employed. Performs an enlargement process on each component image data (in FIG. 1, source image (A), mask image (B), background image (C), hereinafter referred to as component image), A method of constructing enlarged image data using each component image data after the enlargement processing can also be adopted. In such an enlargement process, the importance of the high-quality enlargement process is also increasing.

FIG. 2 is a diagram illustrating a method for performing mask processing using a source image and a mask image.
As shown in FIG. 2, in an image generally expressed in multiple gradations including color (hereinafter referred to as a multi-valued image), a source image (A) and a mask image (B) as a binary image are respectively represented. In some cases, the enlargement process is performed, and the mask process is performed using each image data after the enlargement process.
In general, existing representative methods for enlarging an image include a nearest neighbor method, a linear interpolation method, and a cubic convolution method. When the source image (FIG. 2A) and the mask image (FIG. 2B) are enlarged by the nearest neighbor method and mask processing is performed, the nearest neighbor method uses each pixel value after enlargement as its pixel value. Is the method of using the pixel value of the pixel with the closest distance when the image is inversely mapped onto the original image (that is, the method in which pixels having the same pixel value are continuous). As a result, jaggies are generated at the edges and the image has a mosaic shape.
In addition, when the source image is enlarged using a linear interpolation method or cubic convolution method with higher image quality than the nearest neighbor method, the composite image after mask processing, which corresponds to the edge portion of the mask image There is a problem in the pixel values around the edge of each other.

  FIG. 3 shows a specific example in which the source image and the mask image, which are the component image shown in FIG. 1, are enlarged by the linear interpolation method and the nearest neighbor method, respectively. In the linear interpolation method, it is assumed that pixel values between pixels change linearly, and pixel values of four pixels in the vicinity of a point obtained by inversely mapping the enlarged pixel are linearly interpolated to obtain a pixel value. It is. For this reason, as shown in FIG. 3C, the pixel value of the edge portion of the source image changes in pixels surrounded by a thick frame. Therefore, when mask processing is performed using the mask image after the enlargement process shown in FIG. 3B and the source image after the enlargement process shown in FIG. 3C, the edge of the mask image is related to the edge portion of the source image. There arises a problem that the pixel value at the position corresponding to the portion changes. Here, FIG. 3 shows a specific example of enlarging the source image using the linear interpolation method, but the same phenomenon occurs even when the cubic convolution method or the like is used for the enlargement process of the source image.

As an attempt to solve the image quality problem of these synthesized images, for example, a method such as Patent Document 1 has been proposed. In the technique described in Patent Document 1, in a portion where an image subjected to smoothing correction and an image that is not a correction target overlap, the pixel values of the two overlapping pixels are compared, and the pixel value that is higher (or lower) is compared. By selecting and outputting, the problem of the pixel value change of the composite image is solved. However, the technique described in Patent Document 1 is premised on accurate separation of a correction target image and a non-correction target image, and the problem is not solved in the case of generating a composite image by mask processing using two image data. .
In addition to the above-mentioned defect in the edge part due to the mask process, the above-mentioned linear interpolation method does not apply to the assumption that the linear interpolation method changes linearly in terms of high-quality enlargement processing. There is a drawback that the whole image becomes blurred. As for the cubic convolution method, an interpolation function that approximates a sinc function (sin (x) / x) is defined based on the sampling theorem, and 16 pixels (X, Since the pixel value after enlargement is obtained by convolution with the approximate interpolation function in the Y direction (4 pixels each), there is a drawback that the reference range is large and the amount of calculation is large. In addition, since the high frequency band is emphasized, there is a drawback that light jaggy occurs at the edge portion and noise components are emphasized.

As an attempt to solve the image quality problem of these enlarged images, new systems such as Patent Document 2 and Patent Document 3 have been proposed.
In the technique described in Patent Document 2, as a method for detecting the degree of change in an original image, edge detection is performed using an edge detection filter, and edge pixels are defined based on the edge detection result. When it is determined that the pixel is an edge pixel, enlargement is performed by the M-cubic method in which the cubic function shape of the cubic convolution method is adjusted. Otherwise, enlargement is performed by the nearest neighbor method. However, since the edge portion having a large degree of change is performed by the M-cubic method, the image quality characteristics of the cubic convolution method are followed, and there is a disadvantage that jaggy generation and noise components are emphasized.

  In the technique described in Patent Document 3, the property of an image is measured in advance by a density histogram for each block, and based on the result, a first interpolation (pattern matching method or nearest neighbor method (NN method)) and a second interpolation are performed. Superimpose the results of (cubic convolution method or M-cubic method). In the pattern matching method, which is one of the first interpolation processes, when a pattern (binarized pattern) generated from a current image block matches a predetermined angle pattern, a predetermined block is used using a reference block including the current image block. An interpolation pixel block for the target pixel is generated by the rule. However, Patent Document 3 also has a problem that jaggy and noise components are emphasized because of superimposition with the M-cubic method.

JP-A-10-336454 JP 2000-188681 A JP 2002-165089 A

  The present invention has been made from the above-described background, and an object thereof is to provide an image processing apparatus that enlarges an image with high image quality.

  In order to achieve the above object, a first image processing apparatus according to the present invention is an image processing apparatus that performs an enlargement process of an input image, and for a target pixel included in the input image, a predetermined pixel including the target pixel is included. A region discriminating unit that discriminates a size image region based on the feature of the image region, a region feature amount calculating unit that calculates a feature amount of the image region discriminated by the region discriminating unit, and a region discriminating unit. For the discriminated image area, an enlarged image area corresponding to the image area is selected by applying one of the plurality of enlargement techniques selected based on the feature quantity calculated by the area feature quantity calculating means. An enlarged image region generating unit for generating, and an image arranging unit for arranging the enlarged image region generated by the enlarged image region generating unit on an output image.

Preferably, the area discriminating unit discriminates the image area by counting the number of gradations in the image area and comparing the number of gradations with a predetermined reference value.
Preferably, the area determination unit determines the image area based on whether or not a pixel having a predetermined value is included in the image area.

Preferably, the area feature amount calculating unit calculates a feature amount of the image region when the image region determined by the region determining unit satisfies a predetermined condition.
Preferably, the region feature amount calculating means calculates a gradation change amount in the image region based on each pixel value in the image region, and uses the gradation change amount as a feature amount.
Preferably, the region feature amount calculating means calculates a gradation change direction in the image region based on each pixel value in the image region, and uses the gradation change direction as a feature amount.

Preferably, the area feature amount calculating means calculates a gradation change in the image area based on each pixel value in the image area, and when the gradation change corresponds to a predetermined pixel value pattern, This pixel value pattern is selected as a feature amount.
Preferably, the area feature value calculating unit calculates a feature value for each color component in the color space, selects one color component based on the calculated feature value, and calculates the feature value calculated for the color component. Is the feature quantity of the image area.

Preferably, the enlarged image area generation unit is configured such that the feature quantity calculated by the area feature quantity calculation unit satisfies a predetermined condition, and the feature quantity of the image area and the feature quantity of the neighborhood area of the image area The enlarged image area is generated by applying an enlargement method for generating an enlarged image area selected based on the pixel values in the neighboring area.
Preferably, the enlarged image area generating means performs edge enhancement processing according to an enlargement ratio, corrects a pixel value in the image area, and generates an enlarged image area using the corrected pixel value. Is applied to generate an enlarged image region.

Preferably, the enlarged image region generation unit selects a pixel value in a neighboring region according to the direction of gradation change calculated by the region feature amount calculation unit, and uses the selected pixel value to enlarge the image. An enlarged image area is generated by applying an enlargement method for generating an area.
Preferably, the enlarged image region generating unit applies an enlargement method for generating an enlarged image region using a predetermined arithmetic expression corresponding to the feature amount calculated by the region feature amount calculating unit, and Is generated.

Preferably, the enlarged image region generation unit is configured such that when the image region determined by the region determination unit satisfies a predetermined condition, the feature amount of the image region calculated by the region feature amount calculation unit is a predetermined condition. When the above condition is satisfied, an enlarged image area is generated by applying an enlargement technique with a small processing load among a plurality of enlargement techniques to the image area.
Preferably, the enlarged image region generating unit applies an enlargement method based on at least the nearest neighbor interpolation method as an enlargement method with a small processing load.

Preferably, the image arranging unit arranges the enlarged image regions generated by the enlarged image region generating unit so as to overlap when the feature amount calculated by the region feature amount calculating unit satisfies a predetermined condition. .
Preferably, when the plurality of enlarged image regions generated by the enlarged image region generating unit overlap each other, the image arranging unit has a plurality of pixel values corresponding to the same pixel with respect to an overlapping portion of these enlarged image regions. The average value of is calculated.
Preferably, the image arrangement unit does not overlap the enlarged image regions generated by the enlarged image region generation unit when the feature amount calculated by the region feature amount calculation unit satisfies a predetermined condition. Deploy.

  A second image processing apparatus according to the present invention is an image processing apparatus that performs an enlargement process of an input image. For a target pixel included in the input image, an image area having a predetermined size including the target pixel is a source. Applying a discriminating means for discriminating whether it is a part or a mask part, and an enlargement method according to the discrimination result by the discriminating means, an enlarged image area corresponding to the image area discriminated by the discriminating means is obtained. An enlarged image area generating means for generating; and a correcting means for correcting boundary portions of the plurality of enlarged image areas generated by the enlarged image area generating means.

  A first image processing method according to the present invention is an image processing method for enlarging an input image, and for an attention pixel included in the input image, an image area having a predetermined size including the attention pixel Discrimination based on the features of the image region, calculating the feature amount of the discriminated image region, and selecting among the plurality of enlargement methods selected based on the calculated feature amount for the discriminated image region One enlarged method is applied to generate an enlarged image area corresponding to the image area, and the generated enlarged image area is arranged in the output image.

  A second image processing method according to the present invention is an image processing method for performing an enlargement process of an input image. For a target pixel included in the input image, an image area having a predetermined size including the target pixel is a source. Determining whether it is a part or a mask part, applying an enlargement method according to the determination result, generating an enlarged image area corresponding to the determined image area, and generating the plurality of generated The boundary portion of the enlarged image area is corrected.

  A first program according to the present invention includes a computer, and in an image processing apparatus that performs an enlargement process of an input image, for a target pixel included in the input image, an image region having a predetermined size including the target pixel An area determining step for determining based on the feature of the image area, an area feature amount calculating step for calculating the feature amount of the determined image area, and the determined image area based on the calculated feature amount Applying one of the selected enlargement methods, an enlarged image region generating step for generating an enlarged image region corresponding to the image region, and arranging the generated enlarged image region in the output image The image placement step is executed by the computer of the image processing apparatus.

  A second program according to the present invention includes a computer, and in an image processing apparatus that performs an enlargement process of an input image, for a target pixel included in the input image, an image region having a predetermined size including the target pixel is a source A discrimination step for discriminating whether the image is a part or a mask portion, and an enlarged image region generation for generating an enlarged image region corresponding to the discriminated image region by applying an enlargement method according to the discrimination result Causing the computer of the image processing apparatus to execute a step and a correction step of correcting boundary portions of the plurality of generated enlarged image regions.

  According to the image processing apparatus of the present invention, a high-quality enlarged image can be obtained at high speed with a relatively light processing load.

[First Embodiment]
A first embodiment of an image processing apparatus according to the present invention will be described.

[Hardware configuration]
A hardware configuration of the image processing apparatus 2 in the present embodiment will be described.
FIG. 4 is a diagram illustrating the hardware configuration of the image processing apparatus 2 to which the image processing method according to the present invention is applied, centering on the control apparatus 20.
As illustrated in FIG. 4, the image processing apparatus 2 includes a control device 20 including a CPU 202 and a memory 204, a communication device 22, a storage device 24 such as an HDD / CD device, an LCD display device or a CRT display device, and a keyboard. A user interface device (UI device) 26 including a touch panel and the like is included.
The image processing apparatus 2 is, for example, a general-purpose computer in which an image enlargement program 4 (described later) is installed. The image processing apparatus 2 acquires image data via the communication device 22 or the storage device 24 and converts the acquired image data to an output resolution. Enlarge accordingly. For example, the image processing apparatus 2 converts the resolution to 600 dpi or 2400 dpi when outputting image data to the printer apparatus 10, and 75 dpi or the like when outputting image data to the UI apparatus 26. Convert to the resolution.

[Image enlargement program]
FIG. 5 is a diagram illustrating a functional configuration of the image enlargement program 4 which is executed by the control device 20 (FIG. 4) and implements the image processing method according to the present invention.
As illustrated in FIG. 5, the image enlargement program 4 includes a storage unit 400, an image block setting unit 410, an image block determination unit 420, an image block feature amount calculation unit 430, an enlarged image block generation unit 440, and an image block arrangement unit 450. Have The image block determination unit 420 includes a gradation number counting unit 422 and a boundary block determination unit 424, and the image block feature amount calculation unit 430 includes an edge strength calculation unit 432, an edge direction estimation unit 434, and an edge pattern selection unit 436. Have. The enlarged image block generation unit 440 includes a high-quality image block generation unit 442 and a high-speed image block generation unit 444.
Note that all or part of the image enlargement program 4 may be realized by hardware such as an ASIC.

  The storage unit 400 temporarily stores image data until the image data is enlarged, and temporarily stores the enlarged image data that has undergone resolution conversion or enlargement processing until it is output to an output device (not shown). The image data is data described in an image format (for example, BMP, TIFF, PNG, etc.) that can be processed by the image processing apparatus 2, and is captured by a digital camera (not shown) or a scanner (not shown). Or image data created by an application program that is created or edited in a personal computer (image processing apparatus 2 or the like). Enlarged image data (image data after being enlarged) is also data in the same image format.

  The image block setting unit 410 sets a predetermined image block size required in the processing of the image block determination unit 420, the image block feature amount calculation unit 430, and the enlarged image block generation unit 440, respectively. In addition, the image block setting unit 410 sequentially cuts out image blocks having a set block size from the input image data stored in the storage unit 400 (for example, in raster scan order), and converts each image block having the block size into an image. It outputs to each of the block determination unit 420, the image block feature value calculation unit 430, and the enlarged image block generation unit 440.

  The image block determination unit 420 sets at least a part of the image blocks sequentially input from the image block setting unit 410 (for example, a rectangular portion in the vicinity of the central portion) as a region of interest, and includes an image block including the region of interest and its peripheral portion. The number of different pixel values (hereinafter referred to as the block gradation number) is counted. In addition, the image block determination unit 420 determines whether there is a pixel having a specific value. Further, the image block determination unit 420 performs segmentation of the image blocks based on the number of block gradations of the image block including the region of interest and its peripheral portion and the determination result of whether or not the pixel includes a specific value. .

  The image block feature amount calculation unit 430 is configured to perform image processing on a block determined by the image block determination unit 420 as an image block whose block gradation number is equal to or greater than a predetermined threshold and does not include a pixel having a specific value. The image feature amount in the attention area that is at least a part of the block is calculated based on each pixel value in the image block including the attention area or a peripheral portion of the attention area. The image feature amount is, for example, edge strength (gradation change amount) or edge angle (direction of gradation change) of the attention area. However, the image feature amount is not limited to these, and the image block feature amount calculation unit 430 calculates, for example, an average value of each pixel value of the attention area, and a value that represents variation of each pixel value of the attention area with respect to the average value ( For example, standard deviation or variance) may be calculated as the image feature amount.

  Further, the image block feature quantity calculation unit 430 compares the image feature quantity calculated for each attention area and the reference value, and performs the attention area separation. By distinguishing the attention area, an image block having a characteristic (for example, an image block including an edge) and an image block having a small feature (for example, an image block having a small change in pixel value) are separated. More specifically, the image block feature amount calculation unit 430 determines that an image block whose calculated image feature amount is equal to or greater than a reference value is a feature block, and an image block whose calculated image feature amount is less than the reference value. Are determined as non-characteristic blocks. In this example, the image block feature amount calculation unit 430 calculates the edge strength (numerical value) of an image block as one of the image feature amounts, and an image block whose calculated edge strength is a reference value or more is a feature block. The image block having the edge strength less than the reference value is determined to be a non-feature block. Note that the feature quantity used for the feature block segmentation is not limited to the edge strength, and may be the above-described standard deviation, variance, or other feature quantities.

  The enlarged image block generation unit 440 includes a plurality of enlargement units, for example, a high quality image block generation unit 442 and a high-speed image block generation unit 444. The enlarged image block generation unit 440 switches a plurality of enlargement means according to the determination result of the image block by the image block determination unit 420 and the image feature amount calculated by the image block feature amount calculation unit 430 to correspond to the attention area. Generate an enlarged image block. For example, when the image block determination unit 420 determines the count of the block gradation number and the presence or absence of a pixel having a specific pixel value, the block gradation number is equal to or less than a predetermined threshold, or When a pixel having a specific pixel value is present in an image block including the region of interest and its peripheral portion, the enlarged image block generation unit 440 generates an enlargement means for the region of interest at high speed. Switching to the enlargement process by the unit 444 generates an enlarged image block.

  Further, for example, when the edge strength is calculated as a numerical value in the image block feature amount calculation unit 430, the enlarged image block generation unit 440 compares the feature amount with a predetermined value, and according to the comparison result, enlargement means Is switched to either the high-quality image block generation unit 442 or the high-speed image block generation unit 444 to generate an enlarged image block. Here, when the feature amount is larger than the predetermined value, the enlarged image block generation unit 440 performs the enlargement process by the high-quality image block generation unit 442 that performs processing with priority on the image quality, and the feature amount is the predetermined value. In the following cases, enlargement processing is performed by the high-speed image block generation unit 444 that performs processing with priority on speed. Thereby, the entire processing time can be shortened. Of course, the feature quantity used for switching the enlargement means is not limited to the edge strength, and the above-described standard deviation and variance, or other various feature quantities can be used.

  The image block arrangement unit 450 sequentially arranges the enlarged image blocks generated by the enlarged image block generation unit 440, and outputs the enlarged image data subjected to resolution conversion or enlargement to the storage unit 400. The arrangement method of the enlarged image blocks will be described later. For example, in addition to the method of sequentially arranging the enlarged image blocks, the enlarged image blocks are sequentially arranged so as to overlap each other, and the sum of the overlapping pixel values is divided by the number of the overlapped pixels. A method of calculating the value can also be employed.

[Image block determination unit]
The image block determination unit 420 will be described in more detail. A case where the attention area is a 2 × 2 pixel size block and the peripheral area including the attention area is a 4 × 4 pixel size block will be described as a specific example.
The gradation number counting unit 422 counts how many gradations the peripheral area including the attention area is composed of. For example, in the case of a 4 × 4 pixel size block, if all pixel values in the block are different, the block gradation number is 16 gradations. If all the pixel values in the block are the same value, the block gradation number is the first floor. Key. Further, the gradation number counting unit 422 compares the counted block gradation number with the reference gradation number (predetermined block gradation number Tr), and the peripheral region including the attention region is like a natural image. It is determined whether it is a multi-tone image block or a limited-tone image block such as a pattern image or a mask image. That is, if the counted block gradation number is larger than the reference gradation number Tr, the gradation number counting unit 422 determines that the block is a multi-gradation image block, and if smaller than the reference gradation number Tr, the gradation number counting unit 422 determines the limited gradation. Discriminated as an image block.
Note that the boundary block determination unit 424 does not perform processing on the area when the block gradation number of the peripheral area including the attention area is smaller than the reference gradation number Tr.

  The boundary block discriminating unit 424 determines that the area for which the gradation number counting unit 422 determines that the block gradation number of the peripheral area including the attention area is larger than the reference gradation number Tr is the foreground pixel. It is determined whether the boundary block (boundary region) is mixed with background pixels. In other words, the boundary block determination unit 424 determines whether the image block is an image block in which at least one pixel having a specific value is present or an image block in which no pixel exists at all, and the determination result is used as the image block. This is output to the feature quantity calculation unit 430 and the like.

The overall operation of the image block determination unit 420 will be described.
FIG. 6 is a flowchart (S10) showing an image block determination process by the image block determination unit 420.
As shown in FIG. 6, in step 100 (S100), the gradation number counting unit 422 counts the number of block gradations in the peripheral area including the attention area.

In step 102 (S102), the gradation number counting unit 422 compares the counted number of block gradations with a predetermined reference gradation number Tr, and determines whether this area is a multi-gradation image block or a limited gradation. It is determined whether it is an image block.
If it is determined that the peripheral area including the attention area is a multi-tone image block, the process proceeds to step 104 (S104) to determine whether the peripheral area is a boundary block, and the peripheral area including the attention area is determined. If it is determined that the block is a limited gradation image block, the process proceeds to step 106 (S106), and the image block determination unit 420 outputs the determination result to the image block feature amount calculation unit 430.

In step 104 (S104), the boundary block determination unit 424 searches for a background pixel in a peripheral region including the region of interest, and determines whether this region is a boundary block. The boundary block discriminating unit 424 discriminates this area as a boundary block when there is even one background pixel in the peripheral area including the attention area, and when there is no background pixel at step 102 (S102). As the gradation number counting unit 422 determines, it is determined as a multi-gradation image block. Further, the process proceeds to step 106 (S106), and the image block determination unit 420 outputs the determination result to the image block feature amount calculation unit 430.
In this way, the image block determination unit 420 determines whether the peripheral region including the attention region is a multi-tone image block, a limited tone image block, or a boundary block.

[Image block feature value calculation unit]
The image block feature quantity calculation unit 430 will be described in more detail. Similar to the description of the image block determination unit 420 described above, a case where the attention area is a 2 × 2 pixel size block and the peripheral area including the attention area is a 4 × 4 pixel size block will be described as a specific example.
The edge strength calculation unit 432 extracts the edge strength G of the region of interest in the image block extracted by the image block setting unit 410 and determined as a multi-tone image block by the image block determination unit 420 using the following equation (1). Calculated by

gx = (a + c−b−d) / 2
gy = (a + b−c−d) / 2
G = gx × gx + gy × gy (1)

In Expression (1), a, b, c, and d are pixel values of each pixel in the region of interest, as illustrated in FIG. gx represents the amount of change in the pixel value in the main scanning direction (left and right direction in FIG. 7), and gy represents the amount of change in the pixel value in the sub scanning direction (up and down direction in FIG. 7).
Note that the edge strength may be calculated by the following formula (2) or the like instead of the value calculated by the above formula (1) alone.
G = | gx | + | gy | (2)
That is, the edge strength G may be calculated as the sum of the absolute value of gx and the absolute value of gy.

The edge strength calculation unit 432 compares the calculated edge strength G with a reference value (predetermined threshold Th) to determine whether the attention area is a feature block or a non-feature block, The determination result is output to the enlarged image block generation unit 440 and the like.
When the edge strength G of the attention area is smaller than the threshold value Th, the edge direction estimation unit 434 and the edge pattern selection unit 436 do not perform processing on the attention area.

The edge direction estimation unit 434 will be described.
FIG. 7 is an explanatory diagram of a specific example of the attention area and the peripheral area, and an example of the edge direction of the attention area. FIG. 7A illustrates the attention area and the peripheral area, and FIG. 7B illustrates the edge direction estimated for the attention area.
As illustrated in FIG. 7A, the region of interest (image region) has a 2 × 2 rectangular region (two each in the main scanning direction and the sub-scanning direction), and the peripheral region is 4 × 4. It has rectangular areas (four each in the main scanning direction and the sub-scanning direction). Each rectangle corresponds to a pixel, and each numeral in the rectangle indicates a pixel value. That is, the attention area is the pixel {a, b, c, d} = {15, 104, 86, 203} near the center.
Hereinafter, the edge direction estimation processing by the edge direction estimation unit 434 will be described using the attention area illustrated in FIG. 7A as a specific example.

FIG. 8 is a flowchart showing edge direction estimation processing (S20) by the edge direction estimation unit 434.
FIG. 9 is a diagram illustrating a reference region selected in the process of S202 of the edge direction estimation process (S20). The hatched portion in FIG. 9 corresponds to the attention area shown in FIG.
As shown in FIG. 8, in step 200 (S200), the edge direction estimation unit 434 calculates the edge angle Θ of the region of interest exemplified in FIG. 7A by the following equation (3).
Θ = arctan (gy / gx) (3)
In FIG. 7A, the pixel value of the attention area is {a, b, c, d} = {15, 104, 86, 203}, and from the equation (1),
gx = −103
gy = −85
And substituting these into equation (3),
Θ = -140.5 °
It becomes.
The direction of the edge angle Θ corresponds to the broken line direction shown in FIG.

  Furthermore, the edge direction estimation unit 434 determines which of the angle ranges in the directions (eight directions) divided every 22.5 ° is the calculated edge angle Θ. In this example, the angle range around the edge angle Θ of 0 ° or ± 180 ° is “direction 0”, the angle range around 22.5 ° or −157.5 ° is “direction 1”, An angle range centered on 45 ° or −135 ° is “direction 2”, an angle range centered on 67.5 ° or −112.5 ° is “direction 3”, and centered on 90 ° or −90 °. An angle range centered on 112.5 ° or −67.5 ° is “direction 5”, and an angle range centered on 135 ° or −45 ° is “direction 6”. ", And an angle range centered on 157.5 ° or -22.5 ° is" direction 7 ". These angle ranges are ± 11.25 ° from their respective centers. Since the edge angle Θ (= −140.5 °) in the above specific example is included in the range of −135 ° ± 11.25 °, the edge angle is “direction 2”.

In step 202 (S202), the edge direction estimation unit 434 estimates the edge direction from the peripheral area (area outside the thick frame) shown in FIG. 7A according to the calculated edge angle Θ of the attention area. Select the reference area to be used for. More specifically, the edge direction estimation unit 434 selects the reference area so as to include a pixel that may be adjacent to the attention area in the direction of the calculated edge angle Θ.
For example, when the edge angle calculated for the attention area is included in “direction 0”, the edge direction estimation unit 434 selects the reference areas (two areas surrounded by a thick line) illustrated in FIG. When the calculated edge angle is included in “direction 4”, the reference area (two areas surrounded by a thick line) illustrated in FIG. 9B is selected, and the calculated edge angle is other than the above. Are included in the directions (directions 1 to 3 and directions 5 to 7), the reference areas (four areas surrounded by thick lines) illustrated in FIG. 9C are selected. In the specific example shown in FIG. 7, since the direction of the edge angle is “direction 2”, the four reference areas shown in FIG. 9C are candidates for selection.
The reference area is not limited to the area illustrated in FIG. 9. For example, in the case of FIG. 9C, the number of reference areas may be 8, or may be set according to each direction.

In step 204 (S204), the edge direction estimation unit 434 calculates the edge angle Θ for each of the selected reference areas in accordance with the equations (1) and (3) as in the processing of S200.
In step 206 (S206), the edge direction estimation unit 434 compares the edge angle calculated for each reference region with the edge angle calculated for the attention region, and a threshold value in which these differences are set in advance. It is determined whether it is smaller than Θth. If the edge angle difference is smaller than the threshold value Θth, the edge direction estimation unit 434 determines that the edge angle of the reference region is an appropriate edge angle and proceeds to the processing of S208, and the edge angle difference is equal to or greater than the threshold value Θth. If it is, it is determined that the edge angle of the reference region is not an appropriate edge angle, and the process proceeds to S210.

In step 208 (S208), the edge direction estimation unit 434 increments the angle reference number. That is, the edge direction estimation unit 434 increments the angle reference number only when it is determined that the edge angle calculated for the reference region is an appropriate edge angle.
The angle reference number is a variable for counting the reference number of the edge angle, and is initialized to “angle reference number 1” for each region of interest.

In step 210 (S210), the edge direction estimation unit 434 determines whether or not the edge angle has been calculated for all the selected reference areas. If the edge angle has been calculated for all the reference areas, the edge direction estimation section 434 In other cases, the process returns to S204 to calculate an edge angle for the next reference area.
In step 212 (S212), the edge direction estimation unit 434 calculates the sum of the edge angle of the attention area and the edge angle of the reference area determined as an appropriate edge angle, and calculates the sum of the calculated edge angles as an angle. The average edge angle divided by the reference number is set as the estimated edge direction of the attention area.

  In the specific example shown in FIG. 7, the edge angle ΘU is ΘU = -149.4 ° from the upper reference region {86, 203, 171, 211}, and the edge angle ΘL is the left reference region { ΘL = −131.2 ° from 10, 15, 20, 86}, and the edge angle ΘD becomes ΘD = −175.2 ° from the lower reference region {1,102,15,104}, and the edge angle ΘR is From the reference region {104, 215, 203, 219} on the right side, ΘR = −141.0 °. The edge angle Θ = −140.5 ° of the attention area is compared with the edge angle of each reference area, and the number of reference areas whose difference is smaller than the threshold Θth is counted as the angle reference number.

  FIG. 10 is an explanatory diagram of an example of the estimated edge direction in the region of interest shown in FIG. For example, in the above-described specific example, if the difference between the edge angle of the attention area for all the reference areas is smaller than the threshold Θth, the sum of the edge angles obtained from the attention area and the four reference areas is −737.3 °. By dividing by the angle reference number 5, the average edge angle is determined to be -147.5 °. Also in this case, the edge direction estimation unit 434 determines whether the average edge angle is included in any of the eight directions, for example, in the same manner as the edge direction of the region of interest described above. In this example, since the average edge angle is −147.5 °, it is included in “direction 1”, and “direction 1” is the estimated edge direction.

In the present embodiment, a grayscale image that is one color element per pixel is described as a specific example, but the present invention is not limited to this. For example, when a color image in the RGB color space of three color elements per pixel is input, the color space data selected by the strength of the edge strength Gr, Gg, Gb in the data of each color component The edge direction estimation process may be performed. More specifically, the image block feature quantity calculation unit 430 calculates the edge strength for each color component, selects the color component that maximizes the calculated edge strength Gr, Gg, and Gb, and selects the selected color component. The feature amount is calculated only for the color component. In this way, it is possible to suppress deterioration in image quality such as color shift at the edge of enlarged image data in a color image.
In this example, for the attention area and the reference area, the edge angle calculated by the expression (1) is classified into any one of the eight directions, but the classification is not limited to the eight directions, and the edge has higher accuracy. If a direction is required, the classification may be done for a larger number of angular regions, such as 12 directions (every 15.0 °), 16 directions (every 12.25 °).

The edge pattern selection unit 436 will be described.
FIG. 11 is a diagram illustrating an edge pattern table used by the edge pattern selection unit 436.
As illustrated in FIG. 11, the edge pattern selection unit 436 has an edge pattern table in which the estimated edge direction and the edge pattern are associated with each other. In the edge pattern table, one or more edge patterns are registered for each estimated edge direction (for example, eight directions) corresponding to the pattern size of the region of interest.
The edge pattern selection unit 436 refers to the edge pattern table and selects an edge pattern corresponding to the estimated edge direction estimated for each region of interest by the edge direction estimation unit 434.
In this example, as illustrated in FIG. 10, since the estimated edge direction with respect to the attention area is estimated to be “direction 1” by the edge direction estimation unit 434, the edge pattern selection unit 436 According to (direction 1), four edge patterns from “pattern 0” to “pattern 3” corresponding to direction 1 are selected from the edge pattern table shown in FIG. Candidate.

The edge pattern selection unit 436 selects one edge pattern from one or more edge patterns that are edge pattern candidates based on the pixel value of the region of interest. A specific method for selecting an edge pattern will be described with reference to FIG.
FIG. 12 is a diagram for explaining a method of selecting an edge pattern corresponding to the attention area shown in FIG.
In this example, since the estimated edge direction is “direction 1”, four edge patterns from “pattern 0” to “pattern 3” are selected as candidate edge patterns as illustrated in FIG. Has been. These edge patterns are expressed as bit patterns as illustrated in FIG. Specifically, the edge pattern is formed into a bit pattern with the white portion being 0 and the others being 1, and bit patterns from “bit pattern 0” to “bit pattern 3” are generated. Note that these bit patterns may be registered in advance in the edge pattern table shown in FIG. 11 as bit tables.

  The edge pattern selection unit 436 determines a bit pattern corresponding to the attention area. Specifically, the edge pattern selection unit 436 calculates an average pixel value in the attention area in accordance with the following expression (4), subtracts the average value from each pixel value in the attention area, and uses the sign as the attention. The pixel value pattern of the region is used.

Mean = (a + b + c + d) / 4
a_sign = a-Mean
b_sign = b-Mean
c_sign = c-Mean
d_sign = d-Mean (4)

In this example, as shown in FIG. 12C, Mean = (15 + 104 + 86 + 203) / 4 = 102, a_sign = −87, b_sign = 2, c_sign = −16, and d_sign = 101. Therefore, the edge pattern selection unit 436 determines these positive and negative signs, and generates a bit pattern (1010) of the region of interest as shown in FIG.
The edge pattern selection unit 436 performs pattern matching between the bit pattern corresponding to the edge pattern candidate illustrated in FIG. 12B and the bit pattern of the region of interest illustrated in FIG. Is determined as the selection pattern. The selected edge pattern is applied to an enlarged image block generation process in the enlarged image block generation unit 440 described later.

  The edge pattern is not limited to the edge pattern shown in FIG. 11. For example, the edge pattern selection unit 436 switches the edge pattern table according to the type of input image data and applies a different edge pattern. Also good. Further, the edge pattern selection unit 436 may increase or decrease the number of edge pattern candidates at each angle.

[Enlarged image block generator]
The enlarged image block generation unit 440 will be described in more detail.
The high-quality image block generation unit 442 performs the edge pattern and estimation for the attention area obtained by the image block feature amount calculation unit 430 with respect to the attention area determined by the image block feature amount calculation unit 430 as the feature block. Enlarged image block generation processing is performed based on the edge direction. Details will be described later.

  The high-speed image block generation unit 444 determines that the region of interest is determined to be the limited gradation image block and the boundary block by the image block determination unit 420, and is determined to be a non-feature block by the image block feature amount calculation unit 430. The enlarged image block generation process is performed on the attention area by nearest neighbor interpolation, which has a lighter processing load than the enlarged image block generation process in the high-quality image block generation unit 442 and can be processed at high speed.

[High-quality image block generator]
The high-quality image block generation unit 442 will be described in more detail.
First, the high-quality image block generation unit 442 uses the size and coefficient emphasis kernel according to the enlargement magnification of the enlargement process, and the image of the attention area and the peripheral area in the image block cut out by the image block setting unit 410 Emphasize the contrast of data.
FIG. 13 is a diagram illustrating an enhancement kernel 446 (edge enhancement kernel) used by the high-quality image block generation unit 442.
As illustrated in FIG. 13, the first enhancement kernel 446 a uses the weighting factors “1.60” and “−0.15” to enhance contrast, and the second enhancement kernel 446 b uses the weighting factor “1”. .20 ”and“ −0.05 ”to enhance contrast. These enhancement kernels are associated with the enlargement magnification of the enlargement process already performed on the target image, and refer to pixel values at different positions using different weighting coefficients.

As illustrated in FIG. 13A, the first enhancement kernel 446a refers to the pixel a, the right pixel b, the pixel c, and the pixel d of the pixel of interest P with reference to the pixel a of these pixels. Each value is multiplied by a weighting coefficient (−0.15) and added to the pixel value of the pixel of interest P multiplied by the weighting coefficient (1.60), and the sum is set as the pixel value of the pixel of interest P.
The second enhancement kernel 446b is applied to an image having an enlargement magnification larger than that of the first enhancement kernel 446a. As illustrated in FIG. 13B, the lower pixel a separated by one pixel from the target pixel P. , The right pixel b, the upper pixel c, and the left pixel d, the pixel value of these pixels is multiplied by a weighting coefficient (−0.05), and the pixel of interest multiplied by the weighting coefficient (1.20) The pixel value of P is added together, and the added value is set as the pixel value of the target pixel P.
For example, when applying the first enhancement kernel 446a, the pixel value P ′ after contrast enhancement is calculated according to the following equation (5).
Pixel value P ′ = 1.60 × P−0.15 × (a + b + c + d) (5)

As described above, the enhancement kernel differs depending on the enlargement magnification of the enlargement processing that has already been performed. Therefore, when the image is enlarged in order of 4 times and 8 times, the pixels having the characteristics of the original image are 2 The pixel is a pixel at a position distant from four images. Therefore, as illustrated in FIG. 13, the high-quality image block generation unit 442 performs enhancement processing with reference to pixels that are further away as the enlargement magnification is higher. For example, the high-quality image block generation unit 442 performs the first enhancement kernel 446a (see FIG. 5) in the first double enlargement process when the double enlargement process is applied twice in succession to realize the quadruple enlargement. 13 (A)) and the second enhancement kernel 446b (FIG. 13B) is applied in the second double enlargement process. The same applies to magnifications of 8 times, 16 times and later.
Further, the position of the pixel to be referred to is not limited to the vertical and horizontal directions as shown in FIG. For example, the high-quality image block generation unit 442 performs contrast enhancement with reference to pixels in an oblique direction, performs contrast enhancement with reference to further distant pixels, or the type and size of processing target image data. The emphasis kernel to be applied may be switched by.

  Next, the high-quality image block generation unit 442 includes the edge pattern and the estimated edge direction with respect to the attention area obtained by the image block feature value calculation section 430, the attention area on which the contrast enhancement processing has been performed as described above, and its surroundings. An enlarged image block for the region of interest is generated using the pixel value of the region.

  FIG. 14 is an explanatory diagram of a specific example of enlarged image block generation processing in the high-quality image block generation unit 442. First, the high-quality image block generation unit 442 calculates a pixel value corresponding to a 3 × 3 image block using a pixel value subjected to contrast enhancement processing based on the edge pattern of the region of interest and the estimated edge direction. FIG. 14A illustrates an example of the attention area and the peripheral area illustrated in FIG. As described above, in the image block feature quantity calculation unit 430, this attention area corresponds to the edge pattern 1010 and the estimated edge direction is determined to be “direction 1”. In the case of the combination of (edge pattern 1010) − (estimated edge direction “1”), the high-quality image block generation unit 442 calculates each pixel corresponding to the 3 × 3 image block as illustrated in FIG. Assuming p0 to p8, the pixel values of p0 to p8 are calculated by the following formula based on the pixel values {a, b, c, d} of the attention area shown in FIG. The combination of the edge pattern and the estimated edge direction is expressed as “(edge pattern) − (estimated edge direction)”.

p0 = a
p1 = (a + b) / 2
p2 = b
p3 = (a + c) / 2
p4 = (b + c) / 2
p5 = (b + d) / 2
p6 = c
p7 = (b + b) / 2
p8 = d

  These calculation formulas are uniquely determined by a combination of (edge pattern) − (estimated edge direction), and pixel values corresponding to 3 × 3 image blocks are calculated.

FIG. 15 is an explanatory diagram of an example of a calculation formula used in the case of another (edge pattern)-(estimated edge direction) combination.
FIG. 15A shows a calculation formula used in the case of (edge pattern 1000) − (estimated edge direction “1”), and pixel values corresponding to 3 × 3 image blocks are calculated by the following formula.

p0 = a
p2 = b
p3 = a
p4 = (b + c) / 2
p5 = (b + d) / 2
p6 = c
p7 = (c + d) / 2
p8 = d
p1 = (p4 + c) / 2

  FIG. 15B shows a calculation formula used in the case of (edge pattern 1100) − (estimated edge direction “5”), and pixel values corresponding to 3 × 3 image blocks are calculated by the following formula.

p0 = a
p1 = (a + b) / 2
p2 = b
p4 = (a + d) / 2
p6 = c
p7 = (c + d) / 2
p8 = d
p3 = (p4 + c) / 2
p5 = (p4 + b) / 2

  FIG. 15C shows a calculation formula used in the case of (edge pattern 1100) − (estimated edge direction “2”), and a pixel value corresponding to a 3 × 3 image block is calculated by the following formula.

p0 = a
p1 = a
p2 = b
p3 = a
p4 = (b + c) / 2
p5 = (b + d) / 2
p6 = c
p7 = (c + d) / 2
p8 = d

  FIG. 15D shows a calculation formula used in the case of (edge pattern 0101) − (estimated edge direction “7”), and pixel values corresponding to 3 × 3 image blocks are calculated by the following formula.

p0 = a
p2 = b
p3 = (a + c) / 2
p4 = (a + d) / 2
p5 = (b + d) / 2
p6 = c
p8 = d
p1 = (p4 + b) / 2
p7 = (p4 + c) / 2

  In the case of other edge patterns as well, pixel values corresponding to 3 × 3 image blocks can be calculated by performing calculation according to the calculation formula corresponding to each edge pattern.

  Next, the high-quality image block generation unit 442 generates a plurality of reference pixels in the peripheral area selected based on the pixel value equivalent to the 3 × 3 image block calculated as described above and the estimated edge direction of the attention area. Are used to generate 4 × 4 image blocks.

  FIG. 16 is an explanatory diagram of a method of selecting the reference pixels r0 to r13 based on the estimated edge direction in the attention area. When the estimated edge direction of the attention area is from direction 1 (22.5 °) to direction 3 (67.5 °), as shown in FIG. 16A, reference pixels r0 to r5 are shown in FIG. A) is selected so that there are 3 pixels from the upper left to the lower and 3 pixels from the lower right to the upper as surrounded by a thick line frame. Further, when the estimated edge direction of the attention area is from the direction 5 (112.5 °) to the direction 7 (157.5 °), as shown in FIG. 3 pixels from the top to the top and 3 pixels from the top right to the bottom. As shown in FIGS. 16A and 16B, four reference pixels r6 to r13 are selected on the upper and lower sides, respectively, regardless of the estimated edge direction. In this way, the reference pixel is selected based on the estimated edge direction in the attention area. Of course, the selection of reference pixels is not limited to selection from two patterns as shown in FIG. 16, and more reference pixel selection patterns may be prepared according to the estimated edge direction. The reference pixel to be selected may be changed according to the estimated edge direction.

  FIG. 17 is an explanatory diagram of a specific example of the process of generating a 4 × 4 pixel enlarged image block. As shown in FIG. 17, using the calculated pixel values p0 to p8 corresponding to the 3 × 3 image block and the reference pixels r0 to r13 selected based on the estimated edge direction in the attention area, the following calculation formula is used. Pixel values (s0 to s15) corresponding to the 4 × 4 pixel enlarged image block are calculated to generate a 4 × 4 enlarged image block.

s0 = 0.2 × r6 + 0.16 × r0 + 0.64 × p0
s1 = 0.2 × r7 + 0.32 × p0 + 0.48 × p1
s2 = 0.2 × r8 + 0.48 × p1 + 0.32 × p2
s3 = 0.2 × r9 + 0.64 × p2 + 0.16 × r1
s4 = 0.08 * r0 + 0.32 * p0 + 0.12 * r2 + 0.48 * p3
s5 = 0.16 × p0 + 0.24 × p1 + 0.24 × p3 + 0.36 × p4
s6 = 0.24 × p1 + 0.16 × p2 + 0.36 × p4 + 0.24 × p5
s7 = 0.32 × p2 + 0.08 × r1 + 0.48 × p5 + 0.12 × r3
s8 = 0.12 * r2 + 0.48 * p3 + 0.08 * r4 + 0.32 * p6
s9 = 0.24 × p3 + 0.36 × p4 + 0.16 × p6 + 0.24 × p7
s10 = 0.36 × p4 + 0.24 × p5 + 0.24 × p7 + 0.16 × p8
s11 = 0.48 × p5 + 0.12 × r3 + 0.32 × p8 + 0.08 × r5
s12 = 0.16 × r4 + 0.64 × p6 + 0.2 × r10
s13 = 0.32 × p6 + 0.48 × p7 + 0.2 × r11
s14 = 0.48 × p7 + 0.32 × p8 + 0.2 × r12
s15 = 0.64 × p8 + 0.16 × r5 + 0.2 × r13

  In this way, 4 × 4 pixel enlarged image blocks (s0 to s15) are generated for the attention area determined as the feature block by the image block feature amount calculation unit 430.

  As described above, the enlarged image block generation unit 440 selects the enlargement process according to the image block determination result by the image block determination unit 420 and the image feature amount calculated by the image block feature amount calculation unit 430. Thus, high-quality enlargement processing with reduced jaggies and enlargement processing with reduced processing load are possible. Specifically, the high-speed image block generation unit 444 uses an enlargement method with a lighter processing load than the high-quality image block generation unit 442, thereby reducing the processing load of the entire enlargement process, reducing the processing time, Enlargement processing can be realized.

[Image block placement section]
The image block arrangement unit 450 will be described in more detail.
The image block arrangement unit 450 sequentially arranges the enlarged image blocks for the region of interest generated by the enlarged image block generation unit 440 by a predetermined method. The image block arrangement unit 450 corrects the boundary portions of the plurality of enlarged image blocks generated by the enlarged image block generation unit 440 and constitutes a correction unit.
FIG. 18 is an explanatory diagram of a specific example in which the 4 × 4 pixel enlarged image blocks generated by the enlarged image block generation unit 440 are arranged. In the example illustrated in FIG. 18, the image block arrangement unit 450 arranges the enlarged image block 0 and the enlarged image block 1 that are sequentially generated so as to overlap each other. The overlapping pixels are arranged so as to average each of the previous pixel values. The overlapping pixels may be calculated by calculating the sum of the overlapping pixels and dividing the sum of the pixel values by the number of overlaps. In this case, the target pixel is selected while being shifted by one pixel, for example, and the enlargement process is performed. Alternatively, the enlarged image blocks can be arranged without overlapping. In this case, the attention areas are selected so as not to overlap.

[Overall operation]
The overall operation (image enlargement process) of the image processing apparatus 2 will be described.
FIG. 19 is a flowchart (S30) showing the overall operation of the image processing apparatus 2.
As shown in FIG. 19, in step 300 (S300), the image block setting unit 410 is a default image block required by the image block determination unit 420, the image block feature amount calculation unit 430, and the enlarged image block generation unit 440. Are set, the image blocks having the set block size are sequentially cut out from the processing target image data stored in the storage unit 400 (for example, in raster scan order), and the cut out image blocks are set as image block determination units. 420, and output to each of the image block feature value calculation unit 430 and the enlarged image block generation unit 440.

  In step 302 (S302), the image block determination unit 420 determines whether the peripheral region including the region of interest in the image block cut out by the image block setting unit 410 is a multi-tone image block or a limited-tone image block. Or a boundary image block. Specifically, the image block determination unit 420 counts the number of block gradations in the peripheral area including the attention area and compares it with a predetermined reference gradation number to determine whether the peripheral area is a multi-gradation image block. It is determined whether it is a gradation image block. Further, the image block determination unit 420 determines an image block including a pixel (background pixel) having a specific pixel value among the image blocks determined to be a multi-tone image block as a boundary image block.

In step 304 (S304), the image block feature amount calculation unit 430 selects an image block whose peripheral region including the attention region is a multi-tone image block based on the image block determination result by the image block determination unit 420. .
When it is determined that the peripheral area including the attention area is a multi-tone image block, the image processing apparatus 2 proceeds to the process of S306 to perform an enlargement process for storing the image feature of the attention area. If it is determined that the block is other than the tonal image block (limited gradation image block and boundary image block), the process proceeds to the process of S318 so as to perform the process of generating the enlarged image block by the nearest neighbor interpolation method for this attention area. .

In step 306 (S306), the image block feature amount calculation unit 430 calculates the edge strength G of the attention area in the input image block using Expression (1).
Note that {a, b, c, d} are each pixel value in the region of interest as illustrated in FIG. When the input image data is not a grayscale image but a color image in, for example, an RGB color space, the image block feature quantity calculation unit 430 performs image blocks for each color component in the R, G, and B color spaces with respect to the region of interest. For each, the edge strengths Gr, Gg, and Gb are calculated using the formula (1), and the image component of the color component that is the maximum edge strength among the Gr, Gg, and Gb is selected, and the edge strength is selected as the region of interest. Edge strength (common to all color components).

In step 308 (S308), the image block feature quantity calculation unit 430 determines whether the attention area is a feature block or a non-feature block based on the edge strength G of the attention area. Specifically, the image block feature amount calculation unit 430 determines the region of interest when the calculated edge strength G is greater than or equal to a predetermined threshold Th (that is, when the amount of gradation change in the region of interest is large). When the calculated edge strength G is less than a predetermined threshold Th (that is, when the gradation change amount in the attention area is small), the attention area is determined as a non-feature block.
When it is determined that the attention area is a feature block, the image processing apparatus 2 proceeds to the process of S310 to perform an enlargement process for storing the image feature of the attention area, and the attention area is a non-feature block. If it is determined, the process proceeds to S318 so as to perform a process of generating an enlarged image block with a small processing load.

In step 310 (S310), the image block feature quantity calculation unit 430 uses the equation (3) for the edge angle Θ of the reference region in the attention region (feature block) and one or more peripheral regions including the attention region. To calculate.
Note that gx and gy are values calculated in the equation (1). The image block feature quantity calculation unit 430 estimates the edge direction θ of the region of interest from the calculated plurality of edge angles Θ. For example, the image block feature quantity calculation unit 430 calculates the average value of the obtained plurality of edge angles Θ, and sets the calculated average value as the estimated edge direction θ.

  In step 312 (S312), the image block feature quantity calculation unit 430 selects an edge pattern using the estimated edge direction θ and the pixel distribution pattern of the region of interest (feature block). The edge pattern is selected from a pattern table (FIG. 11) prepared in advance according to the edge direction and the pixel distribution pattern.

  In step 314 (S314), the high-quality image block generation unit 442 of the enlarged image block generation unit 440 uses the enhancement kernel 446 (FIG. 13) in which the size and the weighting coefficient are set according to the enlargement ratio. Contrast enhancement processing is performed on the image data of the region of interest (feature block) in the image block cut out by the unit 410 and its peripheral region.

  In step 316 (S316), the high-quality image block generation unit 442 of the enlarged image block generation unit 440 includes the edge pattern of the attention area and the estimated edge direction θ of the attention area obtained by the image block feature amount calculation section 430. The 3 × 3 image is calculated by the calculation formulas (FIGS. 14 and 15) corresponding to the edge pattern and the estimated edge direction θ using the attention area and the pixel values in the peripheral area subjected to contrast enhancement in the process of S314. Pixel values (p0 to p8) corresponding to the blocks are generated. Further, the high-quality image block generation unit 442 uses the reference pixels (r0 to r13) selected based on the generated pixel values (p0 to p8) and the estimated edge direction θ to perform the calculation illustrated in FIG. An enlarged image block for the attention area is generated by the equation.

  In step 318 (S318), the high-speed image block generation unit 444 of the enlarged image block generation unit 440 recognizes the attention area or the image block feature amount determined as the limited gradation image block or the boundary image block by the image block determination unit 420. An enlarged image block for the attention area determined as a non-feature block by the calculation unit 430 is generated using the nearest neighbor interpolation method.

In step 320 (S320), the image block arrangement unit 450 sequentially arranges the enlarged image blocks generated by the enlarged image block generation unit 440.
In addition, as a method of arrangement, a method of sequentially arranging the enlarged image blocks generated by the enlarged image block generation unit 440 so as to overlap can be considered. In the case of overlapping, a method of sequentially taking an average with the previous pixel value or a method of dividing the sum of the overlapping previous pixel values by the number of overlaps can be considered. However, as long as the processing speed and the image quality are taken into consideration, any arrangement method may be used as long as the method uses the enlarged image block in the enlarged image block generation unit 440.

  In step 322 (S322), it is determined whether or not the processing from S300 to S320 has been completed for all input image data. If it is determined that the processing has not been completed, the processing returns to S300 and the next. When the process for the pixel block is performed and it is determined that the process is completed for all input image data, the enlargement process ends.

As described above, the image processing apparatus 2 according to the present embodiment determines whether an image area having a predetermined size including the target pixel is an image area having multiple gradations or not, and determines the determined multi-gradation image. A feature amount of the region is calculated, and an enlarged image is switched by switching a plurality of enlargement methods for enlarging the image region of a predetermined size including the target pixel according to the determination result of the image region and the calculated feature amount of the multi-tone image region. An area is generated, and the obtained enlarged image area is arranged by a predetermined method to generate an enlarged output image. By such enlargement processing, it is possible to suppress problems at the boundary with the mask image at the time of image composition.
In addition, the image processing apparatus 2 according to the present embodiment applies high-quality enlargement processing only to characteristic portions (feature blocks), thereby suppressing the overall processing load and enlarging high-quality images for characteristic portions. Since the processing is applied, it is possible to save the feature amount of the characteristic part and obtain a high-quality enlarged image in which image quality defects such as blur and jaggy are suppressed.

[Second Embodiment]
A second embodiment of the image processing apparatus according to the present invention will be described.

[Image enlargement program]
FIG. 20 is a diagram illustrating a functional configuration of the image enlargement program 6 which is executed by the control device 20 (FIG. 4) and implements the image processing method according to the present invention.
As illustrated in FIG. 20, the image enlargement program 6 includes a storage unit 400, a scan line determination unit 600, an image block setting unit 410, an image block determination unit 420, an image block feature amount calculation unit 430, and a high quality image block generation unit. 442, a high-speed enlargement processing unit 650, and an enlarged image integration unit 660. The image block determination unit 420 includes a gradation number counting unit 422 and a boundary block determination unit 424, and the image block feature amount calculation unit 430 includes an edge strength calculation unit 432, an edge direction estimation unit 434, and an edge pattern selection unit 436. Have.
20 that are substantially the same as those shown in FIG. 5 are denoted by the same reference numerals.

  The scan line determination unit 600 sequentially cuts out input image data for each line unit from the input image data stored in the storage unit 400, and how many different pixel values are in the image data for each line unit (hereinafter referred to as this). (Referred to as the number of line gradations). Further, the scan line determination unit 600 separates line-based image data cut out from the input image data according to the number of line gradations.

  The image block setting unit 410 is input image data in units of several lines, in which the scan line determination unit 600 determines that the number of line gradations is equal to or greater than a predetermined reference value, and the set image block size can be cut out. Then, image blocks having the set block size are sequentially cut out (for example, in raster scan order), and each of the image blocks having the block size is extracted as an image block determination unit 420, an image block feature value calculation unit 430, and a high-quality image block generation unit 442. Respectively.

The high-quality image block generation unit 442 uses the image feature amount calculated by the image block feature amount calculation unit 430 to generate an enlarged image block corresponding to the attention area. For the enlargement process in the high-quality image block generation unit 442, it is desirable to apply an enlargement method that retains the features included in the region of interest.
In addition, the high-quality image block generation unit 442 performs the enlargement process only on an image block (that is, a feature block) in which the feature amount calculated by the image block feature amount calculation unit 430 is equal to or greater than a reference value.

  The high-speed enlargement processing unit 650 enlarges the input image data stored in the storage unit 400. The high-speed enlargement processing unit 650 applies an enlargement algorithm that has a smaller processing load than the enlargement process performed by the high-quality image block generation unit 442. Specifically, the high-speed enlargement processing unit 650 enlarges the input image data by applying, for example, the nearest neighbor interpolation enlargement method. Also, the high-speed enlargement processing unit 650 can perform enlargement processing in units of input images or in units of several lines of input images, rather than processing for each image block.

  The enlarged image integration unit 660 integrates the enlarged image block enlarged by the high-quality image block generation unit 442 and the enlarged image enlarged by the high-speed enlargement processing unit 650. More specifically, the enlarged image integration unit 660 uses the high-quality image block generation unit 442 only for image blocks (feature blocks) in which the image feature amount calculated by the image block feature amount calculation unit 430 is greater than or equal to a reference value. Apply the enlarged image. Further, the enlarged image integration unit 660 includes an image block (non-feature block) in which the calculated image feature amount is less than a reference value, image data in which the number of line gradations is determined to be equal to or less than the reference value by the scan line determination unit 600, Alternatively, an enlarged image enlarged by the high-speed enlargement processing unit 650 is applied to an image block whose block gradation number is determined to be equal to or less than the reference value by the image block determination unit 420 or includes a pixel having a specific value. Thus, one enlarged image is generated.

[Scanline determination unit]
The scan line determination unit 600 will be described in more detail.
The scan line determination unit 600 counts how many gradations the image data for each line is composed of. For example, if all pixel values in one line are the same value, the number of line gradations is one gradation. Further, the scan line determination unit 600 determines image data for each line by comparing the counted number of line gradations with a reference gradation number (a predetermined line gradation number Tl). In this example, by setting the number of reference gradations to Tl = 2, the scan line determination unit 600 determines that the image data for each line is multi-tone image data such as a natural image or a mask image. Which binary image data is selected. In other words, the scan line determination unit 600 determines that the counted line gradation number is multi-gradation image data if it is larger than the reference gradation number Tl, and determines that it is binary image data if it is smaller than the reference gradation number Tr. To do. When the number of line gradations of the image data is smaller than the reference gradation number Tl (that is, in the case of binary image data), enlargement processing by the high-speed enlargement processing unit 650 is performed.
Further, in this example, the input image data is described on the assumption that the input image data is a grayscale image that is one color element per pixel, but is not limited to such an image. For example, when color image data in the RGB color space of three color elements per pixel is input, the scan line determination unit 600 counts the number of line gradations in each color component data, and all the color component data Only when the number of line tones is smaller than Tl (= 2), it may be determined as binary image data.

[Enlarged image integration section]
The enlarged image integration unit 660 will be described in more detail.
The enlarged image integration unit 660 integrates the enlarged image block for the region of interest generated by the high-quality image block generation unit 442 and the enlarged image output from the high-speed enlargement processing unit 650.
FIG. 21 is an explanatory diagram of a specific example in which the enlarged image block of 4 × 4 pixels generated by the high-quality image block generation unit 442 and the enlarged image output from the high-speed enlargement processing unit 650 are integrated.
As illustrated in FIG. 21, the enlarged image integration unit 660 sequentially arranges the enlarged image block 0 and the enlarged image block 1 that are sequentially generated at corresponding positions on the enlarged image output from the high-speed enlargement processing unit 650. Integrate. At this time, the enlarged image integration unit 660 may be arranged so as to replace each pixel value on the enlarged image with each pixel value (S0 to S15) of the enlarged block, or each pixel value on the enlarged image and the enlarged image. You may arrange | position so that each pixel value (S0-S15) of a block may overlap.
When the corresponding positions on the enlarged image of the enlarged image blocks (enlarged image block 0 and enlarged image block 1 in FIG. 21) overlap, the enlarged image integration unit 660 overlaps each enlarged image block. Alternatively, the sum of the overlapping pixels may be calculated, and each pixel value may be calculated by dividing the sum of the pixel values by the number of overlaps.

[Overall operation]
The overall operation (image enlargement process) of the image processing apparatus 2 will be described.
FIG. 22 is a flowchart (S40) showing the overall operation of the image processing apparatus 2 in the present embodiment.
As shown in FIG. 22, in step 400 (S400), the scan line determination unit 600 sequentially cuts out the input image data for each line unit from the input image data stored in the storage unit 400, and the image for each line unit. Count the number of line tones in the data.

In step 402 (S402), the scan line determination unit 600 determines whether the input image data for each line unit is multi-tone image data or binary image data. Specifically, whether the image data is multi-gradation image data or binary image data by comparing the line gradation number counted in step 400 (S400) with a predetermined reference gradation number Tl (eg, Tl = 2). Determine if it exists.
When the image processing apparatus 2 determines that the image data for each line unit is binary image data, the image processing apparatus 2 proceeds to the process of S424 in order to enlarge the image data at high speed, and the image data for each line unit is large. If it is determined that the image data is gradation image data, the process proceeds to S404 to perform block-unit enlargement processing.

  In step 404 (S404), the image block setting unit 410 sets the default image block sizes required by the image block determination unit 420, the image block feature amount calculation unit 430, and the high-quality image block generation unit 442, respectively. The image block of the set block size is cut out sequentially (for example, in raster scan order) from the line-by-line image data determined as multi-gradation image data by the scan line determination unit 600, and each cut out image block is converted into an image block. The data are output to the determination unit 420, the image block feature value calculation unit 430, and the high-quality image block generation unit 442, respectively.

  In step 406 (S406), the image block determination unit 420 determines whether the peripheral region including the attention region in the image block cut out by the image block setting unit 410 is a multi-tone image block or a limited-tone image block. Or a boundary image block. Specifically, the image block determination unit 420 counts the number of block gradations in the peripheral area including the attention area and compares it with a predetermined reference gradation number to determine whether it is a multi-gradation image block or a limited gradation image. Determine if it is a block. Further, the image block determination unit 420 determines a block including a pixel (background pixel) having a specific pixel value from among the image blocks determined to be a multi-tone image block as a boundary image block.

In step 408 (S408), the image block feature quantity calculation unit 430 selects an image block whose peripheral region including the region of interest is a multi-tone image block based on the image block determination result by the image block determination unit 420. .
When it is determined that the image processing device 2 is a multi-tone image block, the image processing apparatus 2 proceeds to the processing of S410 to perform an enlargement process for storing the image feature of the region of interest, and other than the multi-tone image block (limited floor). If it is determined that the block is a toned image block and a boundary image block), the process returns to S404 and the next image block is processed.

  In step 410 (S410), the image block feature quantity calculation unit 430 calculates the edge strength G of the attention area in the input image block using Expression (1). In addition, {a, b, c, d} is each pixel value in the attention area as illustrated in FIG. When the input image data is not a grayscale image but a color image in, for example, an RGB color space, the image block feature quantity calculation unit 430 performs image blocks for each color component in the R, G, and B color spaces with respect to the region of interest. For each, the edge strengths Gr, Gg, and Gb are calculated using the formula (1), and the image component of the color component that is the maximum edge strength among the Gr, Gg, and Gb is selected, and the edge strength is selected as the attention area. Edge strength (common to all color components).

In step 412 (S412), the image block feature quantity calculation unit 430 determines whether the attention area is a feature block or a non-feature block based on the edge strength G of the attention area. Specifically, the image block feature amount calculation unit 430 determines the region of interest when the calculated edge strength G is greater than or equal to a predetermined threshold Th (that is, when the amount of gradation change in the region of interest is large). When the calculated edge strength G is less than a predetermined threshold Th (that is, when the gradation change amount in the attention area is small), the attention area is determined as a non-feature block.
When it is determined that the attention area is a feature block, the image processing apparatus 2 proceeds to the processing of S414 to perform an enlargement process for storing the image feature of the attention area, and determines that the attention area is a non-feature block. If so, the process returns to S404 to process the next image block.

  In step 414 (S414), the image block feature value calculation unit 430 uses the expression (3) for the edge angle Θ of the reference area in the attention area (feature block) and one or more peripheral areas including the attention area. To calculate. Note that gx and gy are values calculated in the equation (1). The image block feature quantity calculation unit 430 estimates the edge direction θ of the attention area from the calculated plurality of edge angles Θ. For example, the image block feature quantity calculation unit 430 calculates the average value of the obtained plurality of edge angles Θ, and sets the calculated average value as the estimated edge direction θ.

  In step 416 (S416), the image block feature value calculation unit 430 selects an edge pattern using the estimated edge direction θ and the pixel distribution pattern of the region of interest (feature block). The edge pattern is selected from a pattern table (FIG. 11) prepared in advance according to the edge direction and the pixel distribution pattern.

  In step 418 (S418), the high-quality image block generation unit 442 uses the enhancement kernel 446 (FIG. 13) in which the size and the weighting coefficient are set according to the enlargement ratio, and the image cut out by the image block setting unit 410 Contrast enhancement processing is performed on the image data of the attention area (feature block) in the block and its peripheral area.

  In step 420 (S420), the high-quality image block generation unit 442 performs contrast enhancement in the processing of S418 and the edge pattern of the attention area and the estimated edge direction θ of the attention area obtained by the image block feature value calculation section 430. Pixels corresponding to a 3 × 3 image block using a calculation formula (FIGS. 14 and 15) corresponding to the edge pattern and the estimated edge direction θ using the pixel values in the applied attention area and the surrounding area Generate values (p0-p8). Further, the high-quality image block generation unit 442 uses the reference pixels (r0 to r13) selected based on the generated pixel values (p0 to p8) and the estimated edge direction θ to perform the calculation illustrated in FIG. An enlarged image block for the attention area is generated by the equation.

  In step 422 (S422), the image processing apparatus 2 determines whether or not the processing from S404 to S420 has been completed for the image data for each line unit, and if it is determined that the processing has not been completed, S404. Returning to the process, the process for the next pixel block is performed, and if it is determined that the process is completed for the input image data for each line unit, the process proceeds to S424.

  In step 424 (S424), the high-speed enlargement processing unit 650 inputs the input image data stored in the storage unit 400 for each image or every several lines, and performs enlargement processing by the nearest neighbor interpolation method. The high-speed enlargement processing unit 650 performs enlargement processing after the enlargement processing (processing from S404 to S420) by the high-quality image block generation unit 442 is completed. The enlargement process may be performed in parallel, or the enlargement process may be performed before the enlargement process by the high-quality image block generation unit 442.

In step 426 (S426), the enlarged image integration unit 660 integrates the enlarged image generated by the high-quality image block generation unit 442 and the enlarged image enlarged by the high-speed enlargement processing unit 650.
Note that the enlarged image integration unit 660 simply replaces with the enlarged image generated by the high-quality image block generation unit 442 as the integration method of the enlarged pixels, or the enlarged image generated by the high-quality image block generation unit 442. And a method of performing an averaging process on the enlarged image generated by the high-speed enlargement processing unit 650 may be applied. That is to say, if the enlarged image integration unit 660 uses the enlarged image generated by the high-quality image block generation unit 442, various integration methods can be applied in consideration of the processing speed and the image quality.

As described above, the image processing apparatus 2 in this embodiment determines whether the image data for each line unit is image data having multiple gradations or binary data, and for image data having multiple gradations. Calculating a feature amount of an image region in which an image region having a predetermined size including the target pixel has a gradation equal to or higher than a reference value, and using the calculated feature amount and the pixel value of the input image data, an enlarged image is calculated. Is generated. Further, the image processing device 2 generates an enlarged image by enlarging the input image data based on the nearest neighbor method, not the above-described enlargement method, regardless of the number of gradations and the feature amount, An enlarged image generated by the enlargement method based on the image is arranged at a corresponding position on the enlarged image by a predetermined method to obtain an enlarged output image.
In this way, the image processing apparatus 2 can suppress problems at the boundary with the mask image at the time of image composition. In addition, since the image processing apparatus 2 in the present embodiment applies the high-quality enlargement process only to the characteristic part (feature block), the overall processing load can be suppressed. Furthermore, since the image processing apparatus 2 applies the high-quality enlargement process to the characteristic part, the characteristic amount of the characteristic part is stored, and a high-quality enlarged image in which image quality defects such as blurring and jaggy are suppressed is stored. Obtainable.

It is a figure which illustrates the composition of image data. It is a figure which illustrates the method of performing a mask process using a source image and a mask image. The specific example which expanded the source image and mask image which are the component image shown in FIG. 1 with the linear interpolation method and the nearest neighbor method, respectively is shown. It is a figure which illustrates the hardware constitutions of the image processing apparatus 2 with which the image processing method concerning this invention is applied centering on the control apparatus 20. FIG. It is a figure which illustrates the functional structure of the image expansion program 4 which is performed by the control apparatus 20 (FIG. 4) and implement | achieves the image processing method concerning this invention. It is a flowchart (S10) which shows the image block determination process by the image block determination part 420. FIG. It is explanatory drawing of the example of an attention area and a peripheral area, and an example of the edge direction of an attention area. It is a flowchart which shows the edge direction estimation process (S20) by the edge direction estimation part 434. FIG. It is a figure which illustrates the reference field selected in processing of S202 of edge direction presumption processing (S20). It is explanatory drawing of an example of the estimated edge direction in the attention area | region shown in FIG. It is a figure which illustrates the edge pattern table used by the edge pattern selection part 436. It is a figure explaining the selection method of the edge pattern corresponding to the attention area shown in FIG. It is a figure which illustrates the emphasis kernel 446 (edge emphasis kernel) used by the high quality image block generation part 442. It is explanatory drawing of the specific example of the production | generation process of the enlarged image block in the high quality image block production | generation part 442. FIG. It is explanatory drawing of an example of the calculation formula used in the case of the combination of (edge pattern)-(estimated edge direction). It is explanatory drawing of the selection method of the reference pixels r0-r13 based on the estimated edge direction in an attention area. It is explanatory drawing of the specific example of a production | generation process of the enlarged image block of 4x4 pixel. It is explanatory drawing of the specific example which arrange | positions the 4x4 pixel enlarged image block produced | generated by the enlarged image block production | generation part 440. FIG. It is a flowchart (S30) which shows the whole operation | movement of the image processing apparatus 2 in 1st Embodiment. It is a figure which illustrates the functional structure of the image expansion program 5 which is performed by the control apparatus 20 (FIG. 4) and implement | achieves the image processing method concerning this invention. It is explanatory drawing of the specific example which integrates the 4x4 pixel enlarged image block produced | generated by the high quality image block production | generation part 442, and the enlarged image output from the high-speed expansion process part 650. FIG. It is a flowchart (S40) which shows the whole operation | movement of the image processing apparatus 2 in 2nd Embodiment.

Explanation of symbols

2 ... Image processing device 10 ... Printer device 20 ... Control device 202 ... CPU
204 ... Memory 22 ... Communication device 24 ... Storage device 26 ... UI device 4 ... Image enlargement program 400 ... Storage unit 410 ... Image block setting unit 420 ... Image block Judgment unit 422 ... gradation number counting unit 424 ... boundary block discrimination unit 430 ... image block feature quantity calculation unit 432 ... edge strength calculation unit 434 ... edge direction estimation unit 436 ... edge Pattern selection unit 440 ... enlarged image block generation unit 442 ... high quality image block generation unit 444 ... high speed image block generation unit 446a, 446b ... enhanced kernel 450 ... image block arrangement unit

Claims (22)

An image processing apparatus for enlarging an input image,
Area discriminating means for discriminating an image area of a predetermined size including the target pixel with respect to the target pixel included in the input image;
Area feature amount calculating means for calculating the feature amount of the image area determined by the area determining means;
The image area determined by the area determining means is applied to one of the plurality of enlargement techniques selected based on the feature amount calculated by the area feature value calculating means to correspond to the image area. An enlarged image area generating means for generating an enlarged image area to be
An image processing apparatus comprising: an image arrangement unit that arranges an enlarged image region generated by the enlarged image region generation unit in an output image. The image processing apparatus according to claim 1, wherein the region determination unit determines the image region by counting the number of gradations in the image region and comparing the number of gradations with a predetermined reference value. The image processing apparatus according to claim 1, wherein the area determination unit determines an image area based on whether or not a pixel having a predetermined value is included in the image area. The image processing apparatus according to claim 1, wherein the area feature amount calculation unit calculates a feature amount of the image area when the image area determined by the area determination unit satisfies a predetermined condition. The image processing apparatus according to claim 1, wherein the region feature amount calculating unit calculates a gradation change amount in the image region based on each pixel value in the image region, and uses the gradation change amount as a feature amount. . The image processing apparatus according to claim 1, wherein the region feature amount calculating unit calculates a gradation change direction in the image region based on each pixel value in the image region, and uses the gradation change direction as a feature amount. . The area feature amount calculating means calculates a gradation change in the image area based on each pixel value in the image area, and when the gradation change corresponds to a predetermined pixel value pattern, the pixel value pattern The image processing device according to claim 1, wherein the image processing device is selected as a feature amount. The area feature quantity calculating means calculates a feature quantity for each color component in the color space, selects one color component based on the calculated feature quantity, and calculates the feature quantity calculated for the color component in the image area. The image processing apparatus according to claim 1, wherein the image processing apparatus is a feature amount. The enlarged image area generation unit is configured to determine, for an image area in which the feature quantity calculated by the area feature quantity calculation unit satisfies a predetermined condition, a feature quantity of the image area, a feature quantity of a neighborhood area of the image area, and a neighborhood area The image processing apparatus according to claim 1, wherein an enlarged image region is generated by applying an enlargement method for generating an enlarged image region selected based on a pixel value in the image. The enlarged image area generation means applies an enlargement method that performs edge enhancement processing according to an enlargement ratio, corrects a pixel value in the image area, and generates an enlarged image area using the corrected pixel value. The image processing apparatus according to claim 1, wherein an enlarged image area is generated. The enlarged image region generation unit selects a pixel value in a neighboring region according to the direction of gradation change calculated by the region feature amount calculation unit, and generates an enlarged image region using the selected pixel value. The image processing apparatus according to claim 1, wherein an enlarged image region is generated by applying an enlargement method. The enlarged image region generation unit generates an enlarged image region by applying an enlargement method for generating an enlarged image region using a predetermined arithmetic expression corresponding to the feature amount calculated by the region feature amount calculation unit. Item 10. The image processing device according to any one of Items 1 to 9. The enlarged image region generation unit is configured when the image region determined by the region determination unit satisfies a predetermined condition and when the feature amount of the image region calculated by the region feature amount calculation unit satisfies a predetermined condition. The image processing apparatus according to claim 1, wherein an enlarged image region is generated by applying an enlargement method with a small processing load among a plurality of enlargement methods to the image region. The image processing apparatus according to claim 13, wherein the enlarged image region generation unit applies an enlargement method based on at least a nearest neighbor interpolation method as an enlargement method with a small processing load. The image arranging unit arranges the enlarged image regions generated by the enlarged image region generating unit so as to overlap when the feature amount calculated by the region feature amount calculating unit satisfies a predetermined condition. The image processing apparatus according to any one of 14. When the plurality of enlarged image regions generated by the enlarged image region generating unit overlap each other, the image placement unit calculates an average value of a plurality of pixel values corresponding to the same pixel with respect to an overlapping portion of these enlarged image regions. The image processing apparatus according to claim 15. The image arranging unit arranges the enlarged image regions generated by the enlarged image region generating unit so as not to overlap when the feature amount calculated by the region feature amount calculating unit satisfies a predetermined condition. The image processing apparatus according to any one of 1 to 14. An image processing apparatus for enlarging an input image,
A discriminating means for discriminating whether an image area of a predetermined size including the target pixel is a source part or a mask part for the target pixel included in the input image;
Applying an enlargement method according to the determination result by the determination unit, an enlarged image region generation unit that generates an enlarged image region corresponding to the image region determined by the determination unit;
An image processing apparatus comprising: correction means for correcting boundary portions of a plurality of enlarged image areas generated by the enlarged image area generating means. An image processing method for enlarging an input image,
For a target pixel included in the input image, an image area having a predetermined size including the target pixel is determined based on the characteristics of the image area,
Calculating a feature amount of the determined image region;
Applying one enlargement method among a plurality of enlargement methods selected based on the calculated feature amount for the determined image region, and generating an enlarged image region corresponding to the image region,
An image processing method for arranging the generated enlarged image area in an output image. An image processing method for enlarging an input image,
For the target pixel included in the input image, it is determined whether the image area of a predetermined size including the target pixel is a source part or a mask part,
Applying an enlargement method according to the determination result to generate an enlarged image region corresponding to the determined image region,
An image processing method for correcting boundary portions of the generated plurality of enlarged image regions. In an image processing apparatus that includes a computer and performs an enlargement process of an input image,
An area determination step of determining an image area of a predetermined size including the target pixel for the target pixel included in the input image based on the characteristics of the image area;
A region feature amount calculating step for calculating a feature amount of the determined image region;
An enlarged image region that generates an enlarged image region corresponding to the image region by applying one of the plurality of enlargement methods selected based on the calculated feature amount for the determined image region Generation step;
A program that causes a computer of the image processing apparatus to execute an image placement step of placing the generated enlarged image region on an output image. In an image processing apparatus that includes a computer and performs an enlargement process of an input image,
A determination step for determining whether the image area of a predetermined size including the target pixel is a source part or a mask part for the target pixel included in the input image;
Applying an enlargement method according to the determination result, an enlarged image region generation step for generating an enlarged image region corresponding to the determined image region;
A program that causes a computer of the image processing apparatus to execute a correction step of correcting boundary portions of the plurality of generated enlarged image regions.

Images