JP2006236317A - Image processing method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image processing method which makes it possible to obtain an image which has high quality and a high resolution without leaving boundaries of adjacent blocks detectable visually even in a case where the image has already been subjected to the processing of resolution enhancement (enlargement) of the image using a set of fractal parameters. <P>SOLUTION: The image processing apparatus 100 includes: an image division unit 114 which divides an input image IN into range blocks using L (L is an integer of 2 or more) numbers of division patterns so that at least one of the boundaries of each region in a division pattern varies from the boundaries of each region in the other division patterns; a parameter calculation unit 103 which calculates a set of fractal parameters of each range block of the input image IN so as to obtain L sets of fractal parameters; an image transformation unit 107 which generates L pieces of fractal transformed images using, one by one, the modified sets of fractal parameters obtained according to enlargement ratios; and an image synthesis unit 112 which synthesizes the L pieces of transformed images so as to generate a single synthesized image. The image transformation unit 107 and the image synthesis unit 112 repeat the transformation and synthesis using in sequence the respective sets of fractal parameters until this synthesized image converges. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to an image processing method for increasing the resolution (enlargement) of an image signal.

Conventionally, a method using a fractal coding technique has been proposed as a method for increasing (enlarging) the resolution of an image signal (see, for example, Patent Document 1). In this method, first, fractal parameters are detected for image data of 8 × 8 pixels in the input image. Then, image data of a 16 × 16 pixel fractal image is generated using the fractal parameter. Further, after applying discrete cosine transform (DCT) to each of 8 × 8 pixel image data cut out from the input image and 16 × 16 image data cut out from the fractal image, the two DCT coefficient matrices are synthesized. By applying inverse DCT, a high resolution (enlarged) image is generated.
JP 2000-31294 A

  However, in the conventional method, the process of generating an enlarged image of a 16 × 16 pixel block using the fractal parameter detected in the 8 × 8 pixel block is performed independently for each block. For this reason, resolution conversion with high fidelity can be performed within the block, but since the continuity at the boundary portion of the block is not considered, a phenomenon in which the boundary between adjacent blocks is visually detected easily occurs.

  Therefore, the present invention solves the above-described conventional problems, and even when a high resolution (enlargement) process is performed using a fractal parameter, a boundary between adjacent blocks is not visually detected. An object of the present invention is to provide an image processing method capable of obtaining a high-resolution image with high image quality.

  In order to achieve the above object, an image processing method apparatus according to the present invention is an image processing method for processing an input image using fractal transformation, wherein at least some of the boundaries are different L (L is an integer of 2 or more) ) By dividing the input image into a plurality of areas using different division patterns, and calculating fractal parameters for each area included in the input image for each of the L division patterns. A set of fractal parameters is calculated, a predetermined image is used as a conversion target image, and fractal conversion is performed on the conversion target image using each of the L sets of fractal parameters to generate a fractal image. .

  Here, the image processing method further performs correction according to a predetermined enlargement ratio for each of the L sets of fractal parameters, and generates the fractal image when the fractal image is generated. A fractal image obtained by performing fractal transformation using each of L sets of modified fractal parameters on the conversion target image, and enlarging the input image to increase the resolution, using the image expanded at a rate as the conversion target image May be generated.

  Thus, by using the fractal parameter when increasing (enlarging) the input image, it is possible to generate a high-resolution (enlarged) image with high fidelity within the region. In addition, by using multiple division patterns when dividing an input image into multiple regions, high-quality images with high image quality can be obtained without detecting boundaries between adjacent blocks that were detected in the conventional method. Can do.

  Further, when generating the fractal image, the conversion target image is subjected to fractal conversion using each of the L sets of modified fractal parameters to generate L fractal images, and the image processing method includes: Furthermore, the L fractal images may be combined to generate a combined image.

  Here, when generating the composite image, it is determined whether the generated composite image satisfies a predetermined convergence condition, and when generating the fractal image, the composite image is When it is determined that the convergence condition is not satisfied, the composite image is used as the conversion target image, and fractal transformation is performed again using each of the L sets of modified fractal parameters, and L fractal images are obtained. It may be generated.

  Further, when generating the fractal image, the fractal transformation may be repeated for each of the L sets of modified fractal parameters until a predetermined convergence condition is satisfied.

  Thereby, a high-resolution high-resolution image can be obtained without detecting a boundary between adjacent blocks.

  The image processing method further extracts an edge from the input image, determines a weight of each fractal image when generating the composite image based on the extracted position of the edge, and When dividing into regions, the input image is divided into a plurality of regions based on the positions of the edges, and when generating the composite image, the L fractal images may be combined using the weights. Good.

  As a result, a high-resolution (enlarged) image with high fidelity can be generated for the edge position.

  Further, when generating the fractal image, the predetermined image is used as the conversion target image, and fractal conversion is performed using one correction fractal parameter among the L sets of correction fractal parameters to generate a fractal image. Next, using the generated fractal image as the conversion target image, fractal conversion is performed using other modified fractal parameters to generate a fractal image, and the generated fractal image is sequentially converted to the conversion target image. As another example, a fractal image may be generated by performing fractal transformation using other modified fractal parameters not used in the L sets of modified fractal parameters.

  As a result, when conversion is performed using the second and subsequent sets of modified fractal parameters, the conversion is performed using the fractal image converged using the previous set of modified fractal parameters as the conversion target image. There is no need to synthesize a converged image generated using each set of.

  Note that the present invention can be realized not only as such an image processing method but also as an image processing apparatus using the characteristic steps provided in such an image processing method as a means, or by performing these steps as a computer. It can also be realized as a program to be executed. Needless to say, such a program can be distributed via a recording medium such as a CD-ROM or a transmission medium such as the Internet.

  As is clear from the above description, according to the image processing method of the present invention, the fractal parameter is extracted from the processing target image, and the boundary between adjacent blocks is visually detected by using the fractal parameter. Therefore, the resolution of the target image can be increased.

  Therefore, according to the present invention, a high-resolution (enlarged) image can be generated from the target image, and the practical value is very high nowadays when handling digitized images is rapidly spreading.

  Hereinafter, embodiments of the present invention will be described with reference to FIGS.

(Embodiment 1)
FIG. 1 is a block diagram showing a configuration of an image processing apparatus 100 using the image processing method according to Embodiment 1 of the present invention. As shown in FIG. 1, the image processing apparatus 100 includes an input image memory 101, a control unit 102, a parameter calculation unit 103, a parameter correction unit 104, a parameter memory 105, an initial image generation unit 106, an image conversion unit 107, and a switching unit 108. , L generated image memories 109 to 111, an image composition unit 112, a switching unit 113, and an image dividing unit 114.

  The input image IN is held in the input image memory 101. Here, it is assumed that the input image IN has a size of horizontal M pixels and vertical N pixels.

  The parameter calculation unit 103 sequentially reads the input images IN held in the input image memory 101 and performs processing. The parameter calculation unit 103 performs processing for calculating a fractal parameter for a partial image (range block) of the input image IN.

  The image dividing unit 114 divides the input image IN into a plurality of regions (range blocks) using L patterns (L is an integer of 2 or more) with at least some of the boundaries being different. Here, the division of the input image IN into range blocks is performed so that the range blocks do not overlap each other. An area referred to when the fractal parameter of the range block is calculated is called a domain block. A division pattern for dividing the input image IN into range blocks and domain blocks is designated by the control unit 102. In the control unit 102, a plurality of division patterns can be designated.

  First, as a first division pattern for dividing the input image IN into range blocks, as shown in FIG. 2A, a case where the input image IN is divided into n pixels × n pixels with the upper left end of the input image as the origin (start point) will be described. To do.

  The parameter calculation unit 103 sequentially calculates fractal parameters for the range block ri (i indicates the range block number) obtained by dividing the input image IN. When calculating the fractal parameter, the domain block dj (j indicates the number of the domain block) is cut out from the input image IN, and the parameter is obtained for the domain block. When the range block is obtained by dividing the range block as shown in FIG. 2A, as a division pattern for dividing the input image IN into domain blocks, for example, a square of 2n pixels × 2n pixels as shown in FIG. There are division patterns to be divided. Here, it is assumed that domain blocks can be obtained as domain block d1, domain block d2,... Domain block dj while shifting the starting point pixel by pixel from the input image IN. For the positional relationship between the range block and the domain block, for example, as shown in FIG. 2C, the range block ri refers to the optimum domain block dj.

  The process of the parameter calculation unit 103 will be described in detail. Reduced affine transformation is applied to the pixel value f (x, y) (where x and y indicate the two-dimensional position in the input image IN) of the pixels existing in the domain block dj, and the reduced domain block dj ′ obtain. The pixel value fd ′ (x ″, y ″) of the reduced domain block obtained at this time is defined by (Expression 1) to (Expression 3).

  Here, θ represents the rotation angle of the affine transformation, and is selected from 0, π / 2, π, and 3π / 2 (rad). C takes a value of +1 or -1, and indicates the presence or absence of inversion with respect to an axis passing through the center of the domain block. e and f are constants for correcting a shift in coordinates caused by rotation and reversal. Furthermore, s and o are called a scaling coefficient and an offset coefficient, respectively, and the one that minimizes the error between the reduced domain block and the range block shown in (Equation 4) is selected.

  (Xri, yri) indicates the position (in the input image IN) of the upper left pixel of the range block ri, and (xdj, ydj) indicates the upper left pixel of the domain block dj (in the input image IN). Indicates the position. For the range block ri, a domain block that minimizes the error E obtained by (Expression 4) is selected from among the domain blocks in the input image IN, and (xd, yd), θ, C obtained at that time are selected. , S, o are fractal parameters FPi for the range block ri. The fractal parameter calculated for each range block is output to the parameter correction unit 104.

  Next, an example of a second division pattern for dividing the input image IN into range blocks is shown in FIG. In this case, the start position of the range block in the input image IN is shifted from the upper left end of the input image IN. This deviation can be set to various values, for example, n / 2 pixels only in the horizontal direction (FIG. 3A), n / 2 pixels only in the vertical direction (FIG. 3B), horizontal and A division pattern such as n / 2 pixels (FIG. 3C) can be used in the vertical direction. In this case, the domain block division pattern may be the same division pattern as described above.

  The method for obtaining the fractal parameter using the domain block for each range block thus obtained is the same as the method described above. The obtained fractal parameter is output to the parameter correction unit 104.

  Next, FIG. 4A shows an example of a third division pattern for dividing the input image IN into range blocks. As shown in FIG. 4A, in the third division pattern, the input image IN is divided into an area of m pixels × m pixels inclined by 45 degrees. In this case, when a domain block is cut out from the input image IN, a 2 m pixel × 2 m pixel region inclined by 45 degrees is cut out as shown in FIG. Domain blocks can be cut out by shifting one pixel at a time. The method for obtaining the fractal parameter for each range block using the range block and the domain block thus obtained is the same as the method described above. The obtained fractal parameter is output to the parameter correction unit 104.

  In addition, when the input image IN is divided into range blocks, the third pattern (m pixels × m pixels in which the range block is inclined 45 degrees so that the second division pattern exists with respect to the first division pattern. The input image IN may be divided by shifting the start position.

  Further, when the input image IN is divided into range blocks, by shifting the start position of the range block so that the second divided pattern is a divided pattern in which the start position of the range block of the first divided pattern is shifted. When using different division patterns, the size of the range block may be varied.

  As described above, the image dividing unit 114 divides the input image IN into range blocks using a plurality of division patterns designated by the control unit 102. And the parameter calculation part 103 calculates a fractal parameter with respect to the range block obtained by each division pattern. That is, there are as many fractal parameters as the number of patterns for dividing the input image IN into range blocks. Here, it is assumed that L range block division patterns are used. That is, there are L sets of fractal parameters.

  The parameter correction unit 104 performs a process of correcting the fractal parameter FP obtained by the parameter calculation unit 103 into a fractal parameter FP ′ suitable for the enlargement ratio a. The enlargement factor a is designated by the control unit 102. When the fractal parameter is corrected by the parameter correction unit 104, the position information (xd, yd) of the domain block in the fractal parameter is multiplied by a. The corrected fractal parameter (corrected fractal parameter) FP ′ is held in the parameter memory 105. The parameter correction unit 104 individually performs this process for each of the L sets of fractal parameters.

  The image conversion unit 107 generates a fractal image using the corrected fractal parameter FP ′ one by one. In generating a fractal image, an arbitrary image having a size of aM × aN pixels (a is an enlargement factor) is prepared as an initial image. As the initial image, for example, an image in which all pixels are at a gray level, or an image obtained by enlarging the input image IN by a zero-order hold method, a bilinear method, a bicubic method, or the like can be used. The initial image IT is generated by the initial image generation unit 106 and input to the image conversion unit 107, which becomes a conversion target image.

  Details of fractal image generation in the image conversion unit 107 will be described. First, the conversion target image is divided into partial images (enlarged range block) Ri of an × an pixels as shown in FIG. A correction fractal parameter is applied to the area of each expansion range block Ri, and an image of the expansion domain block is used as a reference image to generate a fractal image. The enlarged domain block is obtained by cutting out a partial image of 2an pixels × 2an pixels from the position (axd, ayd) of the domain block of the modified fractal parameter. The generated fractal image is stored in the generated image memory through the switching unit 108. Here, the fractal image G1 processed using the first set of corrected fractal parameters is generated image memory 1 (109), and the fractal image G2 processed using the second set of corrected fractal parameters is generated image memory 2. In (110), the fractal image GL processed using the L-th modified fractal parameter is stored in the generated image memory depending on the used modified fractal parameter, such as the generated image memory L (111). When the image conversion unit 107 completes the processing for all the range blocks of the conversion target image using a certain set of fractal parameters, the image conversion unit 107 performs conversion processing on the conversion target image using the next set of fractal parameters, and generates the generated fractal image. Are sequentially stored in the generated image memory.

  When the image conversion unit 107 finishes processing the conversion target image using all the modified fractal parameter sets, the image composition unit 112 stores the L fractal images (generated images) held in the generated image memories 1 to L. G1 to GL are combined into one image GI. Here, as a method for synthesizing an image, there are a method for obtaining a median value for each of L pixels existing at the same position, a method for obtaining an average value, a method for synthesizing only pixels close to the center portion of the expanded range block, and the like. is there. The composite image GI generated by the image composition unit 112 is input to the switching unit 113.

  If the composite image GI satisfies the convergence condition, the switch 113 is connected to the terminal d, and the composite image GI is output as the output image OT. If the composite image GI does not satisfy the convergence condition, the switch 113 is connected to the terminal e, and the composite image GI is input to the image conversion unit 107 and processed as a conversion target image. The image conversion unit 107 performs processing using the initial image IT generated by the initial image generation unit 106 as a conversion target image at the start of processing, but thereafter, the composite image GI generated by the image composition unit 112 is used. Processing is performed using the conversion target image. In this way, the processing by the image conversion unit 107 and the image composition unit 112 is repeated until the composite image GI converges. Here, the convergence condition is that if the sum (absolute value sum or square value sum) of the difference between the previous synthesized image generated by the image synthesizing unit 112 and the current synthesized image is equal to or less than a predetermined threshold value, the convergence occurs. There are a method for determining that the image has been converged and a method for determining that the image has converged after being repeatedly processed a predetermined number of times. In this case, whether or not the image has converged is determined by the image composition unit 112, the result is notified to the control unit 102, and the control unit 102 may control the switching unit 113.

  Next, the operation of the image processing apparatus 100 configured as described above will be described. FIG. 6 is a flowchart showing an operation flow in the image processing apparatus 100.

  The image dividing unit 114 divides the input image IN stored in the input image memory 101 into a plurality of range blocks by using at least L division patterns having different boundaries (step S101). Next, the parameter calculation unit 103 calculates a fractal parameter FP for the divided range block (step S102). The parameter correction unit 104 corrects the fractal parameter FP to a fractal parameter FP ′ suitable for the enlargement ratio a (step S103). The image conversion unit 107 uses the initial image generated by the initial image generation unit 106 as a conversion target image (step S104), and generates a fractal image using the corrected fractal parameter FP ′ one by one (step S105). Next, when the image conversion unit 107 finishes processing the conversion target image using all the modified fractal parameter sets, the image composition unit 112 stores the L generated images G1 held in the generated image memories 1 to L. ... GL are combined into one composite image GI (step S106). Then, the image composition unit 112 determines whether or not the composite image GI satisfies the convergence condition (step S107). As a result, if the composite image GI satisfies the convergence condition (Yes in step S107), the composite image GI is output as the output image OT from the switch 113 (step S108). On the other hand, if the composite image GI does not satisfy the convergence condition (No in step S107), the composite image GI is input to the image conversion unit 107, and the image conversion unit 107 thereafter uses the composite image GI as a conversion target image ( Processing is performed using step S109) (step S105). Then, the processes by the image conversion unit 107 and the image composition unit 112 are repeated until the composite image GI converges (steps S105 to S109).

  As described above, in the image processing method of the present invention, an input image is divided into range blocks, and fractal parameters are calculated for each range block. The division into range blocks is performed by a plurality of methods by changing the cutout position or the shape, and fractal parameters are calculated for each of the division methods. Subsequently, an initial image having a size enlarged at a desired enlargement ratio with respect to the size of the input image is prepared, and one set of fractal parameters (corrected according to the enlargement ratio) for the same initial image one by one. To generate a fractal transformed image. Then, an image generated using each set of fractal parameters is combined to generate a single combined image, and conversion and combining using the fractal parameters are repeated until the combined image converges.

  Therefore, by using the image processing method of the present invention, a high-resolution (enlarged) image with high fidelity is generated inside the block by using a fractal parameter when the resolution of the input image is increased (enlarged). Can do. In addition, by using a plurality of range block division patterns when calculating the fractal parameter, it is possible to obtain a high-quality high-resolution image without detecting a boundary between adjacent blocks that has been detected in the conventional method. it can.

  In the image processing method of the present invention, when the input image is divided into range blocks, a pattern (first pattern) for dividing the input image into n pixels × n pixels starting from the upper left end of the input image, the upper left of the input image A pattern (second pattern) divided into n pixels × n pixels starting from a position shifted by several pixels from the end (second pattern), and a pattern (third pattern) divided into areas of m pixels × m pixels rotated by 45 degrees will be described. However, the shape and division pattern of the range block are not limited to these patterns. For example, there are a pattern in which the range block has a shape other than a square such as a rectangle or a parallelogram, and a pattern in which the rotated angle is an angle other than 45 degrees such as 30 degrees or 60 degrees.

  Further, in the image processing method of the present invention, the image conversion unit 107 performs one fractal conversion on the conversion target image, holds it in the generated image memories 109 to 111, and then combines it in the image combining unit 112. However, the conversion processing in the image conversion unit 107 is not limited to once, and the conversion processing may be held in the generated image memories 109 to 111 after performing the conversion processing a plurality of times.

(Modification 1 of Embodiment 1)
The above embodiment can be modified as follows.

  Modification 1 will be described with reference to FIG. FIG. 7 is a block diagram showing a configuration of an image processing apparatus 200 using the image processing method according to the first modification of the first embodiment of the present invention. As illustrated in FIG. 7, the image processing apparatus 200 includes an input image memory 101, a control unit 202, a parameter calculation unit 103, a parameter correction unit 104, a parameter memory 105, an initial image generation unit 106, an image conversion unit 201, and a switching unit 108. , L generated image memories 109 to 111, an image composition unit 112, a switching unit 204, a generated image memory 203, and an image dividing unit 114.

  As can be seen by comparing FIG. 1 and FIG. 7, the image processing apparatus 200 is different from the image processing apparatus 100 in the order of processing after the image conversion unit 201.

  FIG. 8 is a flowchart showing an operation flow in the image processing apparatus 200. Since the processing (steps S101 to S103) from when the input image IN is input to the input image memory 101 to when the corrected fractal parameter FP ′ is held in the parameter memory 105 is the same as in the first embodiment, description thereof is omitted. .

  The image conversion unit 201 sets the initial image generated by the initial image generation unit 106 as a conversion target image (step S104), and sets one set of correction fractal parameters one by one from among the L sets of correction fractal parameters stored in the parameter memory 105. Is used to generate a fractal image (step S201). The image conversion unit 201 holds the generated fractal image in the generated image memory 203. The image conversion unit 201 determines whether the generated image satisfies the convergence condition (step S202). As a result, when it is determined that the generated image has not converged (No in step S202), the generated image is input to the image conversion unit 201 again through the switch 204. Then, the image conversion unit 201 converts the generated image as a conversion target image (step S203) and performs conversion processing using the same set of modified fractal parameters. This process is performed until the generated image converges, and when the convergence condition is satisfied (Yes in step S202), the generated image is held in one of the generated image memories 1 to L through the switches 204 and 108. In this case, the image conversion unit 201 determines whether or not the image has converged, notifies the control unit 202 of the result, and the control unit 202 may control the switching unit 204. Next, the image conversion unit 201 determines whether or not fractal images have been generated using all sets of modified fractal parameters (step S204). As a result, if the fractal image is not generated using all the corrected fractal parameters (No in step S204), the image conversion unit 201 performs the same processing (steps S104 to S203) using the next set of corrected fractal parameters. )I do. Then, L converged images are finally generated by performing processing using the corrected fractal parameters in order. Here, the image G1 ′ processed using the first set of modified fractal parameters is generated image memory 1 (109), and the image G2 ′ processed using the second set of corrected fractal parameters is generated image memory 2. In (110), the image GL ′ processed using the L-th modified fractal parameter is stored in the generated image memory different in accordance with the used modified fractal parameter, such as in the generated image memory L (111).

  If the fractal image is generated using all the modified fractal parameters (Yes in step S204), the image composition unit 112 sets the L generated images G1 ′ to GL ′ held in the generated image memories 1 to L to 1 The images are combined with each other (step S205). Here, as a method for synthesizing an image, there are a method for obtaining a median value for each of L pixels existing at the same position, a method for obtaining an average value, a method for synthesizing only pixels close to the center of an enlarged range block, and the like . The composite image generated by the image composition unit 112 is output as the output image OT (step S206).

  As described above, in the image processing method of the present invention, an input image is divided into range blocks, and fractal parameters are calculated for each range block. This division into range blocks is performed with a plurality of patterns by changing the cutout position or the shape, and fractal parameters are calculated for each of the divided patterns. Subsequently, an initial image having a size enlarged at a desired enlargement ratio with respect to the size of the input image is prepared, and conversion is performed using one set of fractal parameters (corrected according to the enlargement ratio). The conversion process is repeated until the generated image converges. Then, a converged image generated using each set of fractal parameters is synthesized to generate one synthesized image.

  Therefore, by using the image processing method of the present invention, a high-resolution (enlarged) image with high fidelity is generated inside the block by using a fractal parameter when the resolution of the input image is increased (enlarged). Can do. In addition, by using a plurality of range block cutout patterns when calculating the fractal parameter, it is possible to obtain a high-quality high-resolution image without detecting a boundary between adjacent blocks that has been detected in the conventional method. it can.

(Modification 2 of Embodiment 1)
FIG. 9 is a block diagram showing a configuration of an image processing apparatus 300 using the image processing method according to the second modification of the first embodiment of the present invention. As shown in FIG. 9, the image processing apparatus 300 includes an input image memory 101, a control unit 302, a parameter calculation unit 103, a parameter correction unit 104, a parameter memory 105, an initial image generation unit 106, an image conversion unit 301, and a switching unit 204. A generated image memory 203 and an image dividing unit 114.

  FIG. 10 is a flowchart showing an operation flow in the image processing apparatus 300. Since the processing (steps S101 to S103) from when the input image IN is input to the input image memory 101 until the corrected fractal parameter FP ′ is stored in the parameter memory 105 is the same as in the first embodiment, description thereof is omitted. .

  The image conversion unit 301 sets the initial image generated by the initial image generation unit 106 as a conversion target image (step S104), and sets one set of correction fractal parameters one by one from among the L sets of correction fractal parameters held in the parameter memory 105. Is used to generate a fractal image (step S201). The image conversion unit 301 holds the generated fractal image in the generated image memory 203. The image conversion unit 301 determines whether or not the generated image satisfies the convergence condition (step S202). As a result, when it is determined that the generated image has not converged (No in step S202), the generated image is input again to the image conversion unit 301 through the switch 204. Then, the image conversion unit 301 converts the generated image as a conversion target image (step S203) using the same set of modified fractal parameters. This process is performed until the generated image converges. When the convergence condition is satisfied (Yes in step S202), the image conversion unit 301 determines whether or not all sets of modified fractal parameters have been used (step S301). As a result, if not all the modified fractal parameters are used (No in step S301), the image conversion unit 301 sets the generated image that satisfies the convergence condition as a conversion target image (step S302), and the next set of modified fractal parameters. The same processing is performed using. Then, fractal images are generated in order using the corrected fractal parameters (step S303) (steps S201 to S203). If a fractal image is generated using all the modified fractal parameters (Yes in step S301), the finally generated fractal image is output as an output image OT (step S304).

  As described above, in this modification, when performing conversion using the second and subsequent sets of fractal parameters (corrected according to the enlargement ratio), the fractal images converged using the previous set of fractal parameters are converted. Since conversion is performed using the conversion target image, it is not necessary to synthesize a converged image generated using each set of fractal parameters.

(Embodiment 2)
FIG. 11 is a block diagram showing a configuration of an image processing apparatus 400 using the image processing method according to Embodiment 2 of the present invention. In addition, the same code | symbol is attached | subjected about the part similar to Embodiment 1, and description is abbreviate | omitted.

  As shown in FIG. 11, the image processing apparatus 400 includes an edge extraction unit 401 and a weight determination unit 403 in addition to the configuration of the image processing apparatus 100 of the first embodiment.

  The edge extraction unit 401 extracts edges from the input image IN. The method for detecting the edge may be a generally used method, and any method may be used.

  Based on the position of the edge detected by the edge extraction unit 401, the control unit 402 creates L patterns (L is an integer of 2 or more) with at least some of the boundaries being different.

  FIGS. 12A and 12B are diagrams illustrating an example of a division pattern when the input image IN is divided into range blocks. For example, if the edge 11 is detected by the edge extraction unit 401 as shown in FIG. 12A, the control unit 402 sets, for example, a range so that the edge 11 becomes the boundary of the range block as one of the division patterns. A division pattern such as block 12, range block 13, and range block 14 is created. In addition, the control unit 402, as another division pattern, for example, the range block 15, the range block 16, the range block 17, and the range block so that the edge 11 becomes the boundary of the range block as illustrated in FIG. 18 and a range block 19 are created. In this way, the control unit 402 creates L division patterns with at least some of the boundaries being different.

  The image dividing unit 114 divides the input image IN into range blocks using the L division patterns created by the control unit 402.

  The weight determining unit 403 determines the weight of each fractal image when the image combining unit 404 combines L fractal images based on the edge position detected by the edge extracting unit 401.

  FIG. 12C is a diagram for explaining determination of weights. Here, the input image IN is divided into three division patterns, the image conversion unit 107 generates three fractal images using three sets of modified fractal parameters, and the image synthesis unit 404 generates three fractal images. Will be described as a composition. In addition, it is assumed that the edge 21 is detected by the edge extraction unit 401 as shown in FIG.

  The weight determination unit 403, for example, the range block 22 divided by the division pattern 1 in the three division patterns and the range block divided by the division pattern 2 in the area 25 shown by hatching in FIG. 23 and the weight of each of the range blocks 24 divided by the division pattern 3 are determined. In this case, the weight determination unit 403 determines a lower weight for the range block 22 including the edge 21 than the range block 23 and the range block 24 not including the edge 21. For example, the weight is determined to be “1” for the range block 22 and the weight is determined to be “2” for the range block 23 and the range block 24 that do not include the edge 21.

  The image composition unit 404 synthesizes L fractal images using the weights determined by the weight determination unit 403. In the above example, the image composition unit 404 assigns a weight to the region corresponding to the region 25 indicated by hatching in FIG. 12C for the fractal image generated based on the division pattern 1 corresponding to the range block 22. 1 ”for the fractal image generated based on the division pattern 2 corresponding to the range block 23, and“ 2 ”for the weight for the fractal image generated based on the division pattern 3 corresponding to the range block 24. Is set to “2” to synthesize three fractal images. Here, when combining a plurality of fractal images using weights, a method of using a weighted average of pixel values at the same pixel position as the pixel value of the composite image, a method of using a weighted median value as a pixel value of the composite image, and weights There is a method of setting the pixel value having the largest value as the pixel value of the composite image.

  As described above, since an edge is extracted from the input image and L division patterns having different boundaries based on the edge position are created, the boundary between adjacent blocks is not detected. A high-resolution image with high image quality can be obtained. In addition, since the weight of each fractal image when the L fractal images are synthesized is determined based on the extracted edge positions, a high-resolution (enlarged) image with high fidelity can be generated inside the block. it can.

  In this embodiment, L division patterns are created based on the extracted edge positions, and the weights of the fractal images when the fractal images are synthesized are determined. However, the present invention is not limited to this. is not. For example, as with the first embodiment, L division patterns may be determined based on edge positions obtained by extracting only the weights of the fractal images when the fractal images are combined. Further, only L division patterns are created based on the extracted edge positions, and the fractal images are synthesized in the same manner as in the first embodiment without determining the weights of the fractal images when the fractal images are synthesized. It doesn't matter.

  Furthermore, the creation of the division pattern based on the extracted edge position is not limited to the case where the fractal image is generated using the L division patterns as described in the present embodiment and the first embodiment. The present invention can also be applied to a case where a fractal image is generated using only street division patterns.

(Modification 1 of Embodiment 2)
FIG. 13 is a block diagram showing a configuration of an image processing apparatus 500 using the image processing method according to the first modification of the second embodiment of the present invention. In addition, the same code | symbol is attached | subjected about the part similar to Embodiment 1, and description is abbreviate | omitted.

  As illustrated in FIG. 11, the image processing apparatus 500 includes an edge estimation unit 501 and a weight determination unit 502 in addition to the configuration of the image processing apparatus 100 according to the first embodiment.

  The edge estimation unit 501 enlarges the input image IN with the enlargement factor a, and creates an enlarged image having a size of aM × aN pixels. Here, as a method of enlarging the input image IN, for example, a zero-order hold method, a bilinear method, a bicubic method, or the like can be used. Moreover, the edge estimation part 501 detects an edge from the produced enlarged image. The method for detecting the edge may be a generally used method, and any method may be used.

  The weight determining unit 502 determines the weight of each fractal image when the image combining unit 503 combines the L fractal images based on the edge position detected by the edge estimating unit 501. The operations of the weight determination unit 502 and the image composition unit 503 are the same as those of the weight determination unit 403 and the image composition unit 404 of the second embodiment.

  As described above, since an edge is detected after an enlarged image is created, a high-resolution (enlarged) image with higher fidelity can be generated inside the block.

  In each of the above embodiments, the case where the input image is enlarged and the resolution is increased is described, but the present invention is not limited to this. For example, even when the input image is not enlarged, the input image may be processed using the L division patterns as described above.

(Embodiment 3)
Further, by recording a program for realizing the image processing method shown in each of the above embodiments on a recording medium such as a flexible disk, the processing shown in each of the above embodiments can be performed by an independent computer. It can be easily implemented in the system.

  FIG. 14 is an explanatory diagram when the image processing method of each of the above embodiments is implemented by a computer system using a program recorded on a recording medium such as a flexible disk.

  FIG. 14B shows an appearance, a cross-sectional structure, and a flexible disk as seen from the front of the flexible disk, and FIG. 14A shows an example of a physical format of the flexible disk that is a recording medium body. The flexible disk FD is built in the case F, and a plurality of tracks Tr are formed concentrically on the surface of the disk from the outer periphery toward the inner periphery, and each track is divided into 16 sectors Se in the angular direction. ing. Therefore, in the flexible disk storing the program, the program is recorded in an area allocated on the flexible disk FD.

  FIG. 14C shows a configuration for recording and reproducing the program on the flexible disk FD. When the program for realizing the image processing method is recorded on the flexible disk FD, the program is written from the computer system Cs via the flexible disk drive. When the image processing method for realizing the image processing method by the program in the flexible disk is constructed in the computer system, the program is read from the flexible disk by the flexible disk drive and transferred to the computer system.

  In the above description, a flexible disk is used as the recording medium, but the same can be done using an optical disk. Further, the recording medium is not limited to this, and any recording medium such as an IC card or a ROM cassette capable of recording a program can be similarly implemented.

(Embodiment 4)
Further, an application example of the image processing method shown in the above embodiment and a system using it will be described.

  FIG. 15 is a block diagram showing an overall configuration of a content supply system ex100 that implements a content distribution service. The communication service providing area is divided into desired sizes, and base stations ex107 to ex110, which are fixed radio stations, are installed in each cell.

  The content supply system ex100 includes, for example, a computer ex111, a PDA (personal digital assistant) ex112, a camera ex113, a mobile phone ex114, a camera via the Internet ex101, the Internet service provider ex102, the telephone network ex104, and the base stations ex107 to ex110. Each device such as the attached mobile phone ex115 is connected.

  However, the content supply system ex100 is not limited to the combination as shown in FIG. 15, and any combination may be connected. Further, each device may be directly connected to the telephone network ex104 without going through the base stations ex107 to ex110 which are fixed wireless stations.

  The camera ex113 is a device capable of shooting a moving image such as a digital video camera. The mobile phone is a PDC (Personal Digital Communications) system, a CDMA (Code Division Multiple Access) system, a W-CDMA (Wideband-Code Division Multiple Access) system, or a GSM (Global System for Mobile Communications) system mobile phone, Alternatively, PHS (Personal Handyphone System) or the like may be used.

  In addition, the streaming server ex103 is connected from the camera ex113 through the base station ex109 and the telephone network ex104, and live distribution or the like based on the encoded data transmitted by the user using the camera ex113 becomes possible. The encoded processing of the captured data may be performed by the camera ex113 or may be performed by a server or the like that performs data transmission processing. Further, the moving image data shot by the camera 116 may be transmitted to the streaming server ex103 via the computer ex111. The camera ex116 is a device that can shoot still images and moving images, such as a digital camera. In this case, the encoding of the moving image data may be performed by the camera ex116 or the computer ex111. The encoding process is performed in the LSI ex117 included in the computer ex111 and the camera ex116. Note that image encoding / decoding software may be incorporated into any storage medium (CD-ROM, flexible disk, hard disk, etc.) that is a recording medium readable by the computer ex111 or the like. Furthermore, you may transmit moving image data with the mobile telephone ex115 with a camera. The moving image data at this time is data encoded by the LSI included in the mobile phone ex115.

  In this content supply system ex100, the content (for example, video shot of music live) captured by the user with the camera ex113, camera ex116, etc. is encoded and transmitted to the streaming server ex103 as in the above embodiment. On the other hand, the streaming server ex103 distributes the content data to the requested client. Examples of the client include a computer ex111, a PDA ex112, a camera ex113, a mobile phone ex114, and the like that can decode the encoded data. In this way, the content supply system ex100 can receive and reproduce the encoded data at the client, and also realize personal broadcasting by receiving, decoding, and reproducing in real time at the client. It is a system that becomes possible.

  The image encoding device or the image decoding device described in the above embodiments may be used for encoding and decoding of each device constituting this system.

A mobile phone will be described as an example.
FIG. 16 is a diagram illustrating the mobile phone ex115 using the image processing method described in the above embodiment. The mobile phone ex115 includes an antenna ex201 for transmitting and receiving radio waves to and from the base station ex110, a camera such as a CCD camera, a camera unit ex203 capable of taking a still image, a video shot by the camera unit ex203, and an antenna ex201. A display unit ex202 such as a liquid crystal display that displays data obtained by decoding received video and the like, a main body unit composed of a group of operation keys ex204, an audio output unit ex208 such as a speaker for outputting audio, and a voice input To store encoded data or decoded data such as a voice input unit ex205 such as a microphone, captured video or still image data, received mail data, video data or still image data, etc. Recording medium ex207, and slot portion ex20 for enabling the recording medium ex207 to be attached to the mobile phone ex115 The it has. The recording medium ex207 stores a flash memory element, which is a kind of EEPROM (Electrically Erasable and Programmable Read Only Memory), which is a nonvolatile memory that can be electrically rewritten and erased, in a plastic case such as an SD card.

  Further, the cellular phone ex115 will be described with reference to FIG. The cellular phone ex115 controls the power supply circuit ex310, the operation input control unit ex304, and the image coding for the main control unit ex311 which is configured to control the respective units of the main body unit including the display unit ex202 and the operation key ex204. Unit ex312, camera interface unit ex303, LCD (Liquid Crystal Display) control unit ex302, image decoding unit ex309, demultiplexing unit ex308, recording / reproducing unit ex307, modulation / demodulation circuit unit ex306, and audio processing unit ex305 via a synchronization bus ex313 Are connected to each other.

  When the end call and power key are turned on by a user operation, the power supply circuit ex310 activates the camera-equipped digital mobile phone ex115 by supplying power from the battery pack to each unit. .

  The mobile phone ex115 converts the voice signal collected by the voice input unit ex205 in the voice call mode into digital voice data by the voice processing unit ex305 based on the control of the main control unit ex311 including a CPU, a ROM, a RAM, and the like. The modulation / demodulation circuit unit ex306 performs spread spectrum processing, and the transmission / reception circuit unit ex301 performs digital analog conversion processing and frequency conversion processing, and then transmits the result via the antenna ex201. In addition, the cellular phone ex115 amplifies the received signal received by the antenna ex201 in the voice call mode, performs frequency conversion processing and analog-digital conversion processing, performs spectrum despreading processing by the modulation / demodulation circuit unit ex306, and analog audio by the voice processing unit ex305. After conversion into a signal, this is output via the audio output unit ex208.

  Further, when an e-mail is transmitted in the data communication mode, text data of the e-mail input by operating the operation key ex204 of the main body is sent to the main control unit ex311 via the operation input control unit ex304. The main control unit ex311 performs spread spectrum processing on the text data in the modulation / demodulation circuit unit ex306, performs digital analog conversion processing and frequency conversion processing in the transmission / reception circuit unit ex301, and then transmits the text data to the base station ex110 via the antenna ex201.

  When transmitting image data in the data communication mode, the image data captured by the camera unit ex203 is supplied to the image encoding unit ex312 via the camera interface unit ex303. When image data is not transmitted, the image data captured by the camera unit ex203 can be directly displayed on the display unit ex202 via the camera interface unit ex303 and the LCD control unit ex302.

  The image encoding unit ex312 has a configuration including the image encoding device described in the present invention, and an encoding method using the image data supplied from the camera unit ex203 in the image encoding device described in the above embodiment. The encoded image data is converted into encoded image data by compression encoding, and sent to the demultiplexing unit ex308. At the same time, the cellular phone ex115 sends the sound collected by the audio input unit ex205 during imaging by the camera unit ex203 to the demultiplexing unit ex308 as digital audio data via the audio processing unit ex305.

  The demultiplexing unit ex308 multiplexes the encoded image data supplied from the image encoding unit ex312 and the audio data supplied from the audio processing unit ex305 by a predetermined method, and the multiplexed data obtained as a result is a modulation / demodulation circuit unit A spectrum spread process is performed in ex306, a digital analog conversion process and a frequency conversion process are performed in the transmission / reception circuit unit ex301, and then the signal is transmitted through the antenna ex201.

  When receiving data of a moving image file linked to a home page or the like in the data communication mode, the received signal received from the base station ex110 via the antenna ex201 is subjected to spectrum despreading processing by the modulation / demodulation circuit unit ex306, and the resulting multiplexing is obtained. Is sent to the demultiplexing unit ex308.

  In addition, in order to decode the multiplexed data received via the antenna ex201, the demultiplexing unit ex308 separates the multiplexed data to generate an encoded bitstream of image data and an encoded bitstream of audio data. The encoded image data is supplied to the image decoding unit ex309 via the synchronization bus ex313, and the audio data is supplied to the audio processing unit ex305.

  Next, the image decoding unit ex309 is configured to include the image decoding device described in the present invention, and a decoding method corresponding to the encoding method described in the above embodiment for an encoded bit stream of image data. To generate playback moving image data, which is supplied to the display unit ex202 via the LCD control unit ex302, thereby displaying, for example, moving image data included in the moving image file linked to the homepage . At the same time, the audio processing unit ex305 converts the audio data into an analog audio signal, and then supplies the analog audio signal to the audio output unit ex208. Thus, for example, the audio data included in the moving image file linked to the home page is reproduced. The

  It should be noted that the present invention is not limited to the above-described system, and recently, digital broadcasting using satellites and terrestrial waves has become a hot topic. As shown in FIG. Any of the decoding devices can be incorporated. Specifically, in the broadcasting station ex409, the encoded bit stream of the video information is transmitted to the communication or broadcasting satellite ex410 via radio waves. Receiving this, the broadcasting satellite ex410 transmits a radio wave for broadcasting, and receives the radio wave with a home antenna ex406 having a satellite broadcasting receiving facility, such as a television (receiver) ex401 or a set top box (STB) ex407. The device decodes the encoded bit stream and reproduces it. In addition, the image decoding apparatus described in the above embodiment can also be implemented in a playback apparatus ex403 that reads and decodes an encoded bitstream recorded on a storage medium ex402 such as a CD or DVD that is a recording medium. is there. In this case, the reproduced video signal is displayed on the monitor ex404. Further, a configuration in which an image decoding device is mounted in a set-top box ex407 connected to a cable ex405 for cable television or an antenna ex406 for satellite / terrestrial broadcasting, and this is reproduced on the monitor ex408 of the television is also conceivable. At this time, the image decoding apparatus may be incorporated in the television instead of the set top box. It is also possible to receive a signal from the satellite ex410 or the base station ex107 by the car ex412 having the antenna ex411 and reproduce a moving image on a display device such as the car navigation ex413 that the car ex412 has.

  Further, the image signal can be encoded by the image encoding device shown in the above embodiment and recorded on a recording medium. As a specific example, there is a recorder ex420 such as a DVD recorder that records an image signal on a DVD disk ex421 or a disk recorder that records on a hard disk. Further, it can be recorded on the SD card ex422. If the recorder ex420 includes the image decoding device described in the above embodiment, the image signal recorded on the DVD disc ex421 or the SD card ex422 can be reproduced and displayed on the monitor ex408.

  For example, the configuration of the car navigation ex413 may be a configuration excluding the camera unit ex203, the camera interface unit ex303, and the image encoding unit ex312 in the configuration illustrated in FIG. 17, and the same applies to the computer ex111 and the television (receiver). ) Ex401 can also be considered.

  In addition to the transmission / reception type terminal having both the encoder and the decoder, the terminal such as the mobile phone ex114 has three mounting formats: a transmitting terminal having only an encoder and a receiving terminal having only a decoder. Can be considered.

  As described above, the image processing method described in the above embodiment can be used in any of the above-described devices and systems, and by doing so, the effects described in the above embodiment can be obtained.

  It should be noted that the present invention is not limited to the above-described embodiment, and various changes and modifications can be made without departing from the scope of the present invention.

  Each of the functional blocks in the block diagrams shown in FIGS. 1, 7, 9, 11, and 13 is typically realized as an LSI that is an integrated circuit. These may be individually made into one chip, or may be made into one chip so as to include a part or all of them. (For example, the functional blocks other than the memory may be integrated into one chip.)

  The name used here is LSI, but it may also be called IC, system LSI, super LSI, or ultra LSI depending on the degree of integration.

  Further, the method of circuit integration is not limited to LSI, and implementation with a dedicated circuit or a general-purpose processor is also possible. An FPGA (Field Programmable Gate Array) that can be programmed after manufacturing the LSI or a reconfigurable processor that can reconfigure the connection and setting of circuit cells inside the LSI may be used.

  Further, if integrated circuit technology comes out to replace LSI's as a result of the advancement of semiconductor technology or a derivative other technology, it is naturally also possible to carry out function block integration using this technology. Biotechnology can be applied.

  The image processing method according to the present invention has an effect that an input image can be increased in resolution (enlarged) with high image quality, and is useful as an image processing method in storage, transmission, communication, and the like.

1 is a block diagram (Embodiment 1) showing a configuration of an image processing apparatus using an image processing method of the present invention. (A), (b), (c) It is the schematic diagram (Embodiment 1) for demonstrating the image processing method of this invention. (A), (b), (c) It is the schematic diagram (Embodiment 1) for demonstrating the image processing method of this invention. (A), (b) It is a schematic diagram (Embodiment 1) for demonstrating the image processing method of this invention. 1 is a schematic diagram (Embodiment 1) for explaining an image processing method of the present invention. FIG. 3 is a flowchart showing a flow of operations in the image processing apparatus according to the first embodiment. It is a block diagram (modification 1 of Embodiment 1) which shows the structure of the image processing apparatus using the image processing method of this invention. 10 is a flowchart showing a flow of operations in the image processing apparatus according to the first modification of the first embodiment. It is a block diagram (modification 2 of Embodiment 1) which shows the structure of the image processing apparatus using the image processing method of this invention. 10 is a flowchart showing an operation flow in the image processing apparatus according to the second modification of the first embodiment. It is a block diagram (Embodiment 2) which shows the structure of the image processing apparatus using the image processing method of this invention. (A), (b) It is a figure which shows an example of the division pattern at the time of dividing | segmenting the input image IN into a range block, (c) It is a figure for demonstrating determination of a weight. It is a block diagram (modification 1 of Embodiment 2) which shows the structure of the image processing apparatus using the image processing method of this invention. It is explanatory drawing (Embodiment 2) about the recording medium for storing the program for implement | achieving the image processing method of each said embodiment with a computer system, (a) The physical of the flexible disk which is a recording medium main body. An explanatory diagram showing an example of a format, (b) an external view seen from the front of the flexible disk, a sectional structure, and an explanatory diagram showing the flexible disk, and (c) a configuration for recording and reproducing the program on the flexible disk FD. It is explanatory drawing shown. FIG. 10 is a block diagram showing an overall configuration of a content supply system (Embodiment 3). It is an example (Embodiment 3) of the mobile phone using the image processing method. FIG. 10 is a block diagram of a mobile phone (Embodiment 3). It is an example (Embodiment 3) of the system for digital broadcasting.

Explanation of symbols

100, 200, 300, 400, 500 Image processing apparatus 101 Input image memory 102, 202, 302, 402 Control unit 103 Parameter calculation unit 104 Parameter correction unit 105 Parameter memory 106 Initial image generation unit 107, 201, 301 Image conversion unit 108 Switching unit 109 to 111 L generated image memories 112, 404, 503 Image synthesizing unit 113, 204 Switching unit 114 Image dividing unit 203 Generated image memory 401 Edge extracting unit 403 Weight determining unit 501 Edge estimating unit 502 Weight determining unit Cs Computer・ System FD flexible disk FDD flexible disk drive

Claims (16)

An image processing method for processing an input image using fractal transformation,
Dividing the input image into a plurality of regions by using at least a part of L (L is an integer of 2 or more) division patterns having different boundaries,
For each of the L division patterns, L sets of fractal parameters are calculated by calculating fractal parameters for each region included in the input image.
A fractal image is generated by performing fractal conversion on each of the L sets of fractal parameters, using a predetermined image as a conversion target image, and generating the fractal image. The image processing method further includes:
For each of the L sets of fractal parameters, a correction according to a predetermined enlargement ratio is performed,
When generating the fractal image, an image obtained by enlarging the predetermined image at the enlargement ratio is used as the conversion target image, and fractal conversion is performed using each of the L modified fractal parameters for the conversion target image. The image processing method according to claim 1, further comprising: generating a fractal image having a higher resolution by enlarging the input image. When generating the fractal image, the conversion target image is subjected to fractal conversion using each of the L sets of modified fractal parameters to generate L fractal images,
The image processing method further includes:
The image processing method according to claim 2, wherein the L fractal images are combined to generate a combined image. When generating the composite image, determine whether the generated composite image satisfies a predetermined convergence condition,
When generating the fractal image, if it is determined in the determination that the composite image does not satisfy a predetermined convergence condition, the L sets of modified fractals are again set as the conversion target image. The image processing method according to claim 3, wherein fractal transformation is performed using each of the parameters to generate L fractal images. The image processing method according to claim 3, wherein when generating the fractal image, the fractal transformation is repeated for each of the L sets of modified fractal parameters until a predetermined convergence condition is satisfied. The image processing method further includes:
Extracting edges from the input image;
Based on the extracted position of the edge, determine the weight of each fractal image when generating the composite image,
When dividing the plurality of regions, the input image is divided into a plurality of regions based on the positions of the edges,
The image processing method according to claim 3, wherein when generating the synthesized image, the L fractal images are synthesized using the weights. When determining the weight, the weight is determined in a predetermined synthesis area unit, and in the predetermined synthesis area, the weight of the fractal image generated from the area including the edge is determined to be small,
The image processing method according to claim 6, wherein when generating the synthesized image, the L fractal images are synthesized using the weights for each predetermined synthesis area unit. When the fractal image is generated, the predetermined image is used as the conversion target image, and a fractal image is generated by performing fractal conversion using one correction fractal parameter among the L sets of correction fractal parameters. In addition, the generated fractal image is used as the conversion target image to generate a fractal image by performing fractal conversion using other modified fractal parameters, and the generated fractal image is sequentially used as the conversion target image. The image processing method according to claim 2, wherein a fractal image is generated by performing fractal transformation using another modified fractal parameter that is not used in the L sets of modified fractal parameters. The image processing method further includes:
An edge is estimated from an enlarged image obtained by enlarging the input image at the enlargement ratio,
Based on the position of the edge, determine the weight of each fractal image when generating the composite image,
The image processing method according to claim 2, wherein the L fractal images are synthesized using the weights when the synthesized image is generated. The image processing method further includes:
Extracting edges from the input image;
The image processing method according to claim 1, wherein when the image is divided into the plurality of regions, the input image is divided into a plurality of regions based on the positions of the edges. The image processing method according to claim 10, wherein when the image is divided into the plurality of regions, the input image is divided into a plurality of regions so that the edge position is a boundary. The image processing method according to claim 1, wherein when dividing into the plurality of regions, L division patterns having different division start positions are used for the division patterns. The image processing method according to claim 1, wherein when dividing into the plurality of regions, L division patterns having different division shapes for each division pattern are used. An image processing method for processing an input image using fractal transformation,
Extracting edges from the input image;
Dividing the input image into a plurality of regions based on the extracted edge positions;
Calculating a fractal parameter for each of the regions included in the input image;
An image processing method comprising: generating a fractal image by performing fractal conversion on the conversion target image using the fractal parameter with the predetermined image as a conversion target image. An image processing apparatus that processes an input image using fractal transformation,
A dividing unit that divides the input image into a plurality of regions using L (L is an integer of 2 or more) division patterns having different boundaries at least;
Parameter calculating means for calculating L sets of fractal parameters by calculating fractal parameters for each region included in the input image for each of the L division patterns;
Image processing means, comprising: a predetermined image as a conversion target image; and image conversion means for generating a fractal image by performing fractal conversion on the conversion target image using each of the L sets of fractal parameters. apparatus. A program for processing an input image using fractal transformation,
A division step of dividing the input image into a plurality of regions using L (L is an integer of 2 or more) division patterns having at least some of the boundaries;
A parameter calculating step of calculating L sets of fractal parameters by calculating fractal parameters for each region included in the input image for each of the L division patterns;
An image conversion step of generating a fractal image by performing a fractal conversion on the conversion target image using each of the L sets of fractal parameters with the predetermined image as a conversion target image is performed. Program to do.

Images