JPH01302961A - Picture processing device - Google Patents

Abstract

PURPOSE:To binarizing-process a character part by good resolution and a photograph part by good gradation by deciding whether density change is steep or not as a property peculiar to the character part, and discriminating the sort of the picture of a local area. CONSTITUTION:A discriminating means 2 inputs picture information from a line buffer 1, and obtains contrast at the local area and a density difference between neighboring picture elements as feature information. Then, it decides whether the density change is steep or not as the property peculiar to the character part, and discriminates and decides the sort of the picture of the local area. A selector 5 is switched selectively according to this discrimination decision result, and threshold given from a threshold generating means 3 or the threshold such as dither matrix, etc., given from the threshold generating means 4 is extracted selectively. Then, a picture signal having been read out of the buffer 1 and having been delayed by prescribed timing by a delay unit 7 is led to a comparator 6, and is binarizing-processed by the threshold extracted by the selector 5, and is outputted.

Description

[Detailed Description of the Invention] [Objective of the Invention] (Industrial Field of Application) The present invention is capable of processing a document image in which a text portion and a photo portion are mixed, with high resolution of the text portion and high gradation of the photo portion. The present invention relates to an image processing device that can maintain and binarize images.

(Prior Art) In an image processing device (document image processing device) that can handle not only code information but also image information, high-contrast image parts such as characters and line drawings ( The text part) is binarized using a fixed threshold set in advance, and the image part (photo part) with gradation, such as a photograph, is binarized using a pseudo gradation method such as the dither method. There is.

Incidentally, there is a problem in that when a character part is binarized using the dither method, its resolution deteriorates, and when a photographic part is binarized using a fixed threshold value, its gradation is impaired. In order to avoid such problems, conventional methods separate image regions according to their image characteristics and binarize them by adaptively using the above-mentioned fixed threshold or threshold determined by the dither method for each region. It is supposed to be done.

Conventionally, however, the maximum density difference ΔD max of image density in a local area of the image to be processed is detected, and this maximum density difference ΔD l1laX is compared with a predetermined threshold Th to determine whether the local area is a text area or not. The system identifies and determines whether it is a part, and switches the form of the binarization process.

However, in such a processing method, since the value of the maximum density difference ΔD max detected here is small for low-contrast character parts such as so-called faded characters, they are incorrectly determined to be photographic parts. Sometimes. As a result, a problem arose in that the resolution of text portions with low contrast was significantly impaired.

For example, a document P that has been read and input as shown in FIG.
Text area A with high contrast Photography area B with gradation
, and character portions C with low contrast such as faded characters, typical signal levels of image information in each region A, B, and C of the document P are as shown in FIG. 17. Assuming that the dynamic range of image density is given by 8 bits [0 to FF] h, for example, the maximum density difference ΔD m in a local area of (4×4) pixels is
ax is approximately [DD-PP]h in the region A, [10-40]h in the region B, and [10-40]h in the region C. For this reason, [801h is used as the determination threshold Th, ΔDmax>Th...Character part ΔD ma
X≦Th...When determining the type of image of a local area as a photographic portion, a problem arises in that not only the area B but also the area C is erroneously determined as a photographic portion. Furthermore, when [30] h is used as the determination threshold Th, there is a problem that there is a possibility that the area B will be determined as a character portion.

In this way, in conventional devices that identify the type of area by determining the maximum density difference ΔD +++ax using a predetermined threshold value, for document images containing text with low contrast, the text is There are problems in that it is very difficult to binarize well and to binarize photographic areas with good gradation.

(Problems to be Solved by the Invention) As described above, conventional devices cannot perform binary processing on image information in which text and photographs coexist because they cannot simultaneously satisfy the resolution for the text and the gradation for the photograph. However, especially when characters with low contrast are included, there is a problem in that it is very difficult to adaptively control the format of the binarization process.

The present invention has been made in consideration of these circumstances, and its purpose is to provide high resolution for the text portion and high resolution for the photographic portion, even if the image information includes text portions with low contrast. An object of the present invention is to provide an image processing device that can perform binarization with good gradation.

[Configuration of the Invention (Means for Solving the Problems) The image processing apparatus according to the present invention calculates the maximum value of the difference in density between adjacent pixels from image information within a predetermined range including the pixel of interest in an image to be processed, and calculates the maximum value of the difference in density between adjacent pixels. Find the distance between the pixel showing the maximum density and the pixel showing the minimum density from the image information within a predetermined range, and use this distance to normalize the maximum value of the adjacent pixel density difference and calculate the maximum density difference between adjacent pixels for that image. -5 = This maximum density difference between adjacent pixels is discriminated using a predetermined threshold value to determine the type of image formed by the pixel of interest, and a threshold value is set for binarizing the image information of the pixel of interest. It is characterized by the following.

In other words, it is characterized in that the type of image to be processed is identified by effectively utilizing the property of the image that the density difference between adjacent pixels is steeper in the text portion than in the photo portion. .

(Function) According to the present invention, the maximum value of the difference in density between adjacent pixels within a predetermined range as a local area including the pixel of interest in the image to be processed is determined by comparing the pixel exhibiting the maximum density and the minimum density within the predetermined range. This normalized maximum density difference between adjacent pixels is used as characteristic information of the image to identify and judge the 5 image types, so even if the image has low contrast, the difference between adjacent pixels Due to the nature of whether or not the density difference is steep, it is possible to avoid erroneously identifying this area as a photographic area. As a result, even if a document image contains various types of text and photo areas, it is possible to accurately identify the text and photo areas and adaptively set thresholds for binarization. Therefore, it is possible to perform binarization processing on text portions with good resolution and on photographic portions with good gradation.

(Example) Hereinafter, an example of the present invention will be described with reference to the drawings.

No. The figure is a schematic configuration diagram of an image processing device according to an embodiment. Image information read and input by a reading device such as an image scanner is input to this image processing device as, for example, 8-bit digital data per pixel. The line buffer 1 temporarily stores such image information and uses it for the image processing described below.

The identification means 2 inputs the image information output from the line buffer 2 in synchronization with a predetermined clock, calculates the contrast in the local area and the density difference between adjacent pixels from the image information as feature information, and As a characteristic property, it is determined whether the density change is steep or not, and the type of image of the local area is identified and determined. The selection means (selector) 5 is selectively switched according to the identification judgment result, and the first threshold value given from the first threshold generation means 3 is
or a second threshold such as a dither matrix given from the second threshold generation means 4, the image information is
It is selectively extracted as a threshold value for value processing. Then, the image information read from the line buffer 1, delayed by a predetermined timing via the delay means 7, and guided to the comparison means 6 is set to 2 at the threshold value extracted via the selection means 5.
It is converted into a value and output.

Incidentally, here, the first threshold value is dynamically determined by the dynamic threshold value calculation means 8 according to the input image information.

Here, the identification means 2 that executes the image type identification process includes a smoothing circuit 20 that smoothes the image information read out from the line buffer 1, as shown in FIG. Edge enhancement circuit 21 that performs component enhancement processing. Maximum density difference detection circuit 2 that calculates the maximum value of the density difference between adjacent pixels in a predetermined local area from image information subjected to edge enhancement processing in this edge enhancement circuit 21
2. Further, this identification means 2 includes a maximum/minimum density position detection circuit 23 which respectively determines a pixel having the maximum density and a pixel having the minimum density in the local area from the image information read out from the line buffer 1, and this position detection circuit. A distance calculation circuit 24 is provided for calculating the distance between pixels having the maximum density and minimum density determined in step 23. In the discriminating means 2, the dividing circuit 25 normalizes the maximum density difference between adjacent pixels by dividing it by the distance, and the normalized maximum density difference between adjacent pixels is set to a predetermined threshold Th in a comparing circuit 26. It is configured to identify and determine the type of the image information by comparing the image information with the image information, and generate a selection control signal for setting a threshold value for binarizing the image information.

FIG. 2 shows in detail the circuit portions of the smoothing circuit 20 and the edge emphasis circuit 21 as preprocessing sections in the discriminating means 2. As shown in FIG.

This smoothing process and edge enhancement process are performed, for example (8X
8) Assuming that the process is performed targeting a pixel area,
As shown in FIG. 2, an 8-line buffer is used as the line buffer 1, and the smoothing circuit 20 is
For example, it is composed of six smoothing circuits 20a, 20b, ~2Of that realize a (3×3) median filter,
Furthermore, the edge enhancement circuit 21 is composed of four circuits 21a, 21b, to 21d using Laplacian filters.

Therefore, each pixel stored in 8-line buffer 1 has 8
For example, the timing and image information of the bit is shown in Figure 5.
As shown in the chart, the signals are input to the smoothing circuit 20 in time series as indicated by the signal DIN in units of three pixels in accordance with the clock CLK of a predetermined period. A plurality of (six) smoothing circuits 20a constitute the smoothing circuit 20.

The smoothing process in 20b and 2Of is performed by sorting the densities of (3×3) pixels as shown in FIG. This is done by adjusting the concentration. Specifically, as shown in Figure 4 at the first clock (J-3)
Column (1-3) from pixel in row (J-3) column (1+4)
Information on eight pixels up to the pixel in the row is input in parallel, and each of the three adjacent pixels is smoothed.

Then, at the second clock, (J-2) column (1-3
) row pixel to the (J-3) column (1+4) row pixel, and at the third clock, (J
-1) column (1-3) row pixel to (J-3) column (1
+4) Input information for 8 pixels up to the pixels in the row. Then, each time smoothing information for three pixels adjacent in the column direction is obtained,
Smoothing processing is performed again between them, thereby smoothing the image information two-dimensionally.

The image information smoothed in this manner is input to the edge emphasis circuit 21 at the timing SUN.

This edge emphasis circuit 21 calculates the pixel information in a (3×3) pixel area centered on the reference pixel using the Laplacian method based on the smoothed density difference of the reference pixel between the pixels adjacent to each other in the upper, lower, left, and right directions. Edge enhancement processing using a filter is performed as follows.

G (1+1.J) = F (1+1.J)-2F(
+, J) However, 2F (+, J) = F (++1.J) + F
(1-1, J) + F (1, J+1) + F (
1, J-1,)-4x F (1, J), where F (1, J) is the input image signal of I row and J column.

2F (+, J) indicates the Laplacian which is the second-order differential of the input image F (1, J).

The image information subjected to edge enhancement processing by this Laplacian processing is provided to the maximum value detection circuit 22 of adjacent pixel density difference at the EDG timing shown in FIG.

Now, this maximum value detection circuit 22 of adjacent pixel density difference consists of a density difference calculation circuit 22a that calculates the density difference between adjacent pixels, and a density difference calculation circuit 22a that detects the maximum value from the density difference, as roughly shown in FIG. and a maximum value detector 22b.

As shown in FIG. 7, the density difference calculation circuit 22a includes two stages of flip-flop circuit groups 22aa and 22ab that delay-process the image signals of four pixels in parallel, and between the output signals of these flip-flop circuit groups 22aa and 22ab. 9th difference
As shown in the figure, the subtracter group 22ac each calculates the density difference between adjacent pixels.

In this way, information on the adjacent pixel density difference ΔD calculated in the column and row directions by the density difference calculation circuit 22a is transmitted to the four-stage flip-flop circuit group 22ba and the maximum value detection circuit 22bb, as shown in FIG. As shown in FIG. 8, the densities of adjacent pixels in the (4×4) pixel area are collectively compared, and the maximum density difference ΔD maX therein is determined.

On the other hand, the maximum/minimum value detection circuit 23 detects the clock signal CLK from the line buffer 1 as shown in FIG. 11, for example.
Image information input in units of 4 pixels in synchronization with is sent to the comparators 23b and 23c via the selector 23a.

23d and 23e in sequence in the column direction. still,
The selector 23a distributes the image information input column by column to the comparators 23b, 23c, 28d, and 23e by a 2-bit power counter 23g that operates in response to the clock CLK, as shown in FIG. It is performed under controlled motion.

These comparators 23b, 23c, 23d, and 23e compare image information in units of four pixels in the column direction,
The maximum density and minimum density in that column are determined.

The next stage comparators 23h and 23i are the respective comparators 23b and 2
3c.

The signals from 23d and 23e are input at the timing of PDG2, and the maximum and minimum values obtained in the column direction are compared in the row direction, and the maximum and minimum values among them are determined. Through the above comparison process, the maximum and minimum density values within the (4×4) pixel area are determined. At this time, information about the pixel that takes the maximum density value and information about the pixel that takes the minimum density value are obtained at the same time. The information on the maximum density value and the information on the minimum density are, for example, the first
As shown in FIG. 4, the output is in a format to which position information in the (4×4) local area is added.

The distance calculation circuit 24 inputs the positional information of the pixels having the maximum and minimum density values determined in this way and calculates the distance between the pixels, and is configured as shown in FIG. 13, for example. Ru. That is, the subtraction circuits 24a and 24b calculate the distances ΔX and ΔY between the pixels in the X and Y directions, respectively, and from the distance values, the multiplication circuits 24c, 24d,
Using the adder 24e1 and the square value circuit 24f, it is configured to obtain as follows.

The thus determined distance between the pixel having the maximum density and the pixel having the minimum density within the (4×4) pixel area is provided to the above-mentioned division circuit 25.

Then, the maximum density difference ΔD max between adjacent pixels in the local area found by the density difference detection circuit 22 described above is divided by the distance to normalize it, (ΔDmax/
The value L) is given to the comparison circuit 26 and used for determining the image type.

This determination is made by comparing the value (ΔDmax/L) indicating the image characteristics with a predetermined threshold Th, and ΔDmax/L>Th... Pixel Δ of the character area
Dmax/L≦Th...Performed as a pixel in a non-character area. As described above, the type of image constituted by the pixel of interest within a local predetermined range is discriminated and determined, and the selection of the threshold value by the selector 5 is controlled according to the result of this determination.

Note that the first threshold value to be selected by the selector 5 is generated by the dynamic threshold value calculation means 8 according to the image information. Specifically, according to the maximum density value D max and the minimum density value D min determined by the maximum/minimum value detection circuit 23, the threshold value Bh for binarizing the image information is set, for example, as Bh = (DrLlax +Dmin) / It is dynamically determined as 2. On the other hand, the second threshold value for binarizing the photographic area is given as dither pattern information (dither matrix) as shown in FIG. 15, for example. Such threshold value Bh (first threshold value) or the threshold value indicated by the dither pattern (second threshold value) is selectively extracted via the selector 5 based on the above-mentioned identification determination result, and the second threshold value of the image information is Value processing is performed.

According to the present device configured in this way, even if the maximum density difference value is small and the character image has low contrast, it is possible to detect that the character image has a sharp density change between adjacent elements. By effectively utilizing these characteristics, it is possible to reliably determine the low-contrast character image as a character portion, and to set an appropriate threshold for binarizing the character portion. In other words, the maximum density difference ΔD between adjacent pixels in a local area
max, and also find the distance between the pixel that has the maximum density and the pixel that has the minimum density in that local area, and (ΔDmax
/L) Since the type of image is determined using the value as the evaluation value,
It determines whether the density change between adjacent pixels is steep compared to the average density gradient in that local area, and is therefore very reliable regardless of the contrast (density difference) in the local area. Image types can be easily identified.

As a result, even if a document image contains multiple types of information, text portions can be binarized with good resolution, and photographic portions can be binarized with good gradation.

Note that the present invention is not limited to the embodiments described above. For example, the apparatus may be configured to variably set the size of the local area depending on the image to be processed. Further, the means for adaptively generating the threshold value can be modified in various ways, and the dither pattern used for binarizing the photographic area is not particularly limited. Further, the size of the dither matrix is not limited, and the dither pattern can be arranged not only in a dot-distributed manner but also in a dot-concentrated manner. Furthermore, in the embodiment, the density information was directly obtained from the read and input image information, but it is also possible to obtain the feature amount using a value converted from this amount to image density (logarithm of the reciprocal of the reflectance). . others,
The present invention can be implemented with various modifications without departing from the gist thereof.

[Effects of the Invention] As explained above, according to the present invention, the average value indicated by the maximum value of the difference in density between adjacent pixels in a local region of image information and the distance between the maximum density pixel and the minimum density pixel in that local region Since the type of the image part is identified from the density gradient information and the threshold value for binarizing the image information is selected and set, the character part can be binarized with good resolution regardless of its contrast. Furthermore, photographic areas can be binarized with good gradation, which brings about great practical effects. As a result, it is possible to obtain high-quality binarized images that serve as the basis for various types of image processing, and to achieve great effects such as being able to improve the processing efficiency.

[Brief explanation of the drawing]

FIG. 1 is a schematic configuration diagram of an image processing device according to an embodiment of the present invention, FIG. 2 is a diagram showing a specific configuration example of an identification means in the embodiment device, and FIGS. 3 and 4 are an identification device. 5 is a diagram showing the processing operation timing of the embodiment device, and FIG. 6 is a configuration example of a circuit for calculating maximum and minimum values of adjacent pixel density differences in the embodiment device. FIG. Furthermore, FIG. 7 is a diagram showing a specific configuration example of the adjacent pixel density difference calculation circuit, FIGS. 8 and 9 are diagrams showing the concept of the adjacent pixel density difference, and FIG. 10 is a diagram showing the maximum value of the adjacent pixel density difference. FIG. 11 is a diagram showing an example of the configuration of the maximum concentration/minimum concentration detection circuit. FIG. 12 is a diagram showing the operation timing of the maximum/minimum concentration detection circuit. FIG. 13 is the configuration of the distance calculation circuit. FIG. 14 is a diagram showing an example of the concept of maximum density pixels and minimum density pixels and an example of the format of output information thereof, and FIG. 15 is a diagram showing an example of a dither pattern. FIG. 16 is a diagram showing an example of a document image to be processed, and FIG. 17 is a diagram showing the density level of a typical image signal of the image to be processed shown in FIG. DESCRIPTION OF SYMBOLS 1... Line buffer, 2... Identification means, Death... First threshold value generation means, 4... Second threshold value generation means, 5.
. . . Selector, 6 . 23・
Maximum/minimum value detection/position detection circuit, 24... Distance calculation circuit, 25... Division circuit, 26... Comparison circuit. Applicant's agent Patent attorney Takehiko Suzue-20=

Claims (1)

[Claims] means for determining the maximum value of the difference in density between adjacent pixels from image information within a predetermined range including the pixel of interest in the image to be processed; and a distance between a pixel showing the maximum density and a pixel showing the minimum density from the image information within the predetermined range. means for normalizing the maximum value of the adjacent pixel density difference at this distance; and a type of image formed by the pixel of interest by discriminating the normalized maximum adjacent pixel density difference using a predetermined threshold. and a means for determining the image information of the pixel of interest according to the determination result.
An image processing device comprising means for setting a threshold value for converting into a value.