JP5326670B2 - Image processing apparatus and image processing program - Google Patents

Description

  The present invention relates to an image processing apparatus and an image processing program.

  With the widespread use of electronic documents and digital cameras in recent years, there has been a growing demand for high-quality printing of digital images. For example, there is a noise removal technique that suppresses visual roughness of an image.

  As a technique related to this, for example, Patent Document 1 includes an image processing method, an image determination method, an image processing apparatus, and an image processing method that are more visually appealing and capable of high-quality recording with excellent resolution and gradation. 1 / f noise having a spatial frequency characteristic in which the power spectrum is inversely proportional to the spatial frequency is calculated in advance as a noise matrix table and stored in the noise storage unit. In step 1, a noise matrix table corresponding to the image data input to the noise superimposing processing unit is read from the noise storage unit, noise is superimposed in step 2, and it is determined whether processing is completed for all pixels in step 3. If not, the process returns to step 1, and if completed, the process is terminated.

  Further, for example, in Patent Document 2, image noise and gradation existing in the document itself are not deteriorated by taking into account noise perception characteristics of the visual system, without impairing sharpness (color tone or sharpness in the case of a color image). A spatial frequency that is difficult to visually perceive in an image signal processing device that converts an image signal input by an image input device into an image signal suitable for the image output device for the purpose of reducing the visual difference of the adjustment level. A noise generating unit that generates noise having a characteristic and an amount corresponding to the image signal level; and a noise superimposing unit that superimposes the noise generated by the noise generating unit on the image signal. When handling signals, it is possible to use noise superimposing means that superimposes the noise generated by the noise generating means on the color image signal in a color space region where it is difficult to visually perceive. It is.

  Further, for example, Patent Document 3 has an object of simplifying the configuration of the pulse width modulation circuit and reducing the cost. In a printer that enables halftone expression by forming halftone dots, M-bit image data is provided. A halftone processing unit for converting the data into drive pulse width data of N (M> N) bits, each of the plurality of threshold values output from the threshold value matrix is compared with the image data, and the drive pulse is determined according to the comparison result A conversion circuit that generates width data has a random noise superimposed on a plurality of threshold values or image data and compared to suppress the occurrence of tone jump, and the halftone processing unit is a pixel adjacent to the pixel being processed. It is disclosed to have a pulse position determining means for determining a drive pulse position according to the drive pulse width data.

  Also, for example, in Patent Document 4, a signal processing method, a signal output device, a signal processing device, an image processing device, and an image that can set an arbitrary frequency characteristic and generate a noise signal with relatively low resources An object is to provide a forming apparatus, and a pseudo random number generator is configured by a controller, a plurality of LFSRs, an FIR filter, and a normalizer, and each LFSR is initialized so as to have a predetermined phase difference. A noise signal having a desired frequency characteristic is set by setting the filter coefficient of the FIR filter according to the frequency characteristic of the noise signal to be acquired, setting the parameter to the normalizer, and starting the operation of each LFSR. Is disclosed.

  In addition, for example, Patent Document 5 aims to provide an error diffusion processing device in which regular dot connection, generation of unnecessary textures are suppressed, and tone reproducibility is improved, and image data Pi is diffused. Data correction means for correcting based on the error, quantization processing means for generating the output image data Po by quantizing the corrected image data Pm by varying the number of quantization steps based on the line, and quantization It is disclosed that there is provided error distribution amount determination means for obtaining a diffusion error distributed to peripheral pixels before quantization processing based on the error.

JP 2003-162716 A Japanese Patent Laid-Open No. 06-133164 JP 2004-042325 A JP 2005-275733 A JP 2004-236249 A

  An object of the present invention is to provide an image processing apparatus and an image processing program that limit the generation of periodic patterns and roughness of an image and suppress gradation steps that are likely to occur in noise and gradation areas. It is said.

The gist of the present invention for achieving the object lies in the inventions of the following items.
The invention of claim 1 includes an image receiving means for receiving an image, a matrix generating means for generating a matrix based on a function that guarantees a minimum value in a geometrically isotropic and uniform point distribution, Noise addition means for periodically adding a noise to the image received by the image reception means by performing mask processing using the matrix generated by the matrix generation means; and the noise addition means includes the matrix generation means The first case of statically using the matrix generated by or the second case of dynamically allocating and using the matrix within a predetermined range, In the second case, the width of the predetermined range is switched for each pixel, and the maximum value of noise to be superimposed is controlled by the width of the predetermined range .

  According to a second aspect of the present invention, when the matrix generating means determines a point indicating the position of the threshold value which is data in the matrix, the threshold value is still determined for a set of points that are equal to or less than the already determined threshold value. The first point and the second point group in the set within a predetermined range from the first point are determined by the function from the first point and the second point group. The image processing apparatus according to claim 1, wherein a point to which the threshold value is given is determined so that the value is minimized.

  According to a third aspect of the present invention, in the image processing apparatus according to the second aspect, the function is determined by a twice differentiable function having a convexity having a predetermined strength.

The invention of claim 4 further includes a noise removing unit for removing noise of the image received by the image receiving unit, and the noise adding unit adds noise to the image from which noise has been removed by the noise removing unit. it is an image processing apparatus according to any one of claims 1 to 3, characterized in.

The invention according to claim 5 is the image processing apparatus according to any one of claims 1 to 4 , wherein the noise adding means adds noise to a luminance component of an image.

The invention according to claim 6 is the image processing apparatus according to any one of claims 1 to 5 , wherein the noise adding unit adds noise for a gradation region in the image. .

The invention according to claim 7 is a matrix generator for generating a matrix based on an image receiving means for receiving an image and a function that guarantees a minimum value in a geometrically isotropic and uniform point distribution. And a noise adding means for periodically adding a noise to the image received by the image receiving means by performing a mask process using the matrix generated by the matrix generating means, and the noise adding means It is possible to switch between the first case where the matrix generated by the matrix generation means is used statically or the second case where the matrix is dynamically allocated and used within a predetermined range. to, in the case of the second, switching the width of the range defined the advance for each pixel, to and controlling the maximum value of the noise to be superimposed by the width of the range in which the predetermined Is an image processing program.

According to the image processing apparatus of the first aspect, compared to the case where the present configuration is not provided, the gradation step that is likely to occur in the gradation region is limited while the generation of the periodic pattern and roughness of the image is limited. Can be suppressed. And it can respond adaptively about processing speed. Furthermore, compared with the case where the present configuration is not provided, the memory capacity can be suppressed, and gradation steps that are more likely to occur in the gradation region can be suppressed.

  According to the image processing apparatus of the second aspect, it is possible to create a matrix more easily and at a higher speed than in the case where the present configuration is not provided.

  According to the image processing apparatus of the third aspect, it is possible to generate a matrix having noise with less roughness compared to the case where the present configuration is not provided.

According to the image processing apparatus of the fourth aspect , it is possible to suppress the gradation steps that are likely to occur in the noise and gradation areas as compared with the case where the present configuration is not provided.

According to the image processing apparatus of the fifth aspect , processing can be performed at a higher speed than when the present configuration is not provided.

According to the image processing apparatus of the sixth aspect , it is possible to suppress applying noise to an unnecessary area as compared with the case where the present configuration is not provided.

According to the image processing program of the seventh aspect , compared to the case where the present configuration is not provided, the gradation step which is likely to occur in the gradation area is restricted while the generation of the periodic pattern and roughness of the image is limited. Can be suppressed. And it can respond adaptively about processing speed. Furthermore, compared with the case where the present configuration is not provided, the memory capacity can be suppressed, and gradation steps that are more likely to occur in the gradation region can be suppressed.

It is a conceptual module block diagram about the structural example of this Embodiment. It is a flowchart which shows the process example by this Embodiment. It is a flowchart which shows the example of a threshold value matrix production | generation process by this Embodiment. It is explanatory drawing which shows the noise example with a high energy value, and a low noise example. It is explanatory drawing which shows the example of a white noise, and the example of an energy minimization dispersion | distribution pattern. It is explanatory drawing which shows the example of the allocation process of a threshold value matrix. It is explanatory drawing which shows the process example by this Embodiment. It is a block diagram which shows the hardware structural example of the computer which implement | achieves this Embodiment.

In order to help understanding of the present embodiment, the background and the like will be described.
As one of the problems for high-quality printing, there is a technique for suppressing pseudo contour (gradation level difference), which is the main focus of the present embodiment.
For example, in a digital camera image or graphic image print output, there will be a level difference due to color conversion, screen processing performance, or physical characteristics of electrophotography, and the correspondence between the density of the original image and the printed image will no longer be one-to-one. As a result, a gradation step (tone jump) becomes obvious in the gradation area, and is easily perceived as a pseudo contour.
As one method for solving such a problem, a technique for adding noise to an image is known. By adding noise to the image, the change points of the gradation steps can be spatially scattered, and the pseudo contour can be made inconspicuous.

On the other hand, as another problem for high-quality printing, there is a noise removal technique that suppresses visual roughness of an image.
For example, in the print output of a digital camera image, if shooting noise (compression noise, high sensitivity noise, color noise) peculiar to the digital camera image is mixed in the original image, the print image quality is greatly deteriorated. Further, when sharpness correction processing is performed in order to improve the sharpness, the noise becomes more obvious.
For this reason, a noise removal technique for removing such image noise is used, whereby the image quality can be improved by removing the noise.

Both of the above-mentioned two problems are important in improving the image quality, but they have contradictory aspects.
That is, if the noise is applied, the gradation step is reduced, but the roughness is likely to be manifested. If the noise is removed, the roughness is reduced, but the gradation step is likely to be manifested.
From this point of view, it is necessary to appropriately control the quality / quantity of the noise added to the image. As a method for controlling the quality / amount of noise, for example, Patent Document 2 discloses a method of superimposing noise corresponding to the type of document according to the image by an amount corresponding to the type of document. Patent Document 3 discloses a method of superimposing random noise on an image. Patent Document 1 discloses a method for superimposing 1 / f noise. Patent Document 5 discloses a method of superimposing blue noise.

All of the above-described techniques disclose a method for suppressing a gradation step without causing a defect such as roughness, focusing on the quality and quantity of noise.
In particular, blue noise or the like is generally known as noise that is conceptually less rough.
However, there is no guarantee that the noise presented in the above technique is geometrically isotropic and uniform in terms of quality. Even if it is blue noise, the concept of the blue noise pattern itself is an ideal value, and it is unknown to what level it can be realized, and the currently known blue noise pattern creation method has a much smaller roughness than random noise. , Periodic patterns due to slight roughness and low frequency undulations are easily noticeable.
As described above, many techniques for suppressing tone jump by superimposing noise have been disclosed, but there is no guarantee that the superimposed noise is geometrically isotropic and uniform. Periodic patterns and roughness may occur due to noise superposition.

  The outline of the present embodiment is that an unnecessary noise is removed in advance, and a mask created by using an energy function that minimizes an energy value in a geometrically isotropic and uniform point dispersion set, Noise is superimposed only on the luminance component of the image. Note that point dispersion is described in detail in Japanese Patent Laid-Open No. 2006-295899.

Hereinafter, an example of a preferred embodiment for realizing the present invention will be described with reference to the drawings.
FIG. 1 shows a conceptual module configuration diagram of a configuration example of the present embodiment.
The module generally refers to components such as software (computer program) and hardware that can be logically separated. Therefore, the module in the present embodiment indicates not only a module in a computer program but also a module in a hardware configuration. Therefore, the present embodiment also serves as an explanation of a computer program, a system, and a method. However, for the sake of explanation, the words “store”, “store”, and equivalents thereof are used. However, when the embodiment is a computer program, these words are stored in a storage device or stored in memory. It is the control to be stored in the device. In addition, the modules correspond almost one-to-one with the functions. However, in mounting, one module may be composed of one program, or a plurality of modules may be composed of one program. A plurality of programs may be used. The plurality of modules may be executed by one computer, or one module may be executed by a plurality of computers in a distributed or parallel environment. Note that one module may include other modules. Hereinafter, “connection” is used not only for physical connection but also for logical connection (data exchange, instruction, reference relationship between data, etc.).
In addition, the system or device is configured by connecting a plurality of computers, hardware, devices, and the like by a communication means such as a network (including one-to-one correspondence communication connection), and the like. The case where it implement | achieves by etc. is included. “Apparatus” and “system” are used as synonymous terms. “Predetermined” means that the process is determined before the target process, and not only before the process according to this embodiment starts but also after the process according to this embodiment starts. In addition, if it is before the target processing, it is used in accordance with the situation / state at that time or with the intention to be decided according to the situation / state up to that point.

  The image processing apparatus according to the present embodiment adds noise to an image. As shown in FIG. 1, the image reception module 110, the noise removal module 120, the threshold matrix generation module 130, the noise addition module 140, An image output module 150 is included.

  The image reception module 110 is connected to the noise removal module 120, receives an image, and passes the image to the noise removal module 120. Accepting an image means, for example, taking a picture with a CCD (Charge-Coupled Device), reading an image with a scanner, a camera, etc., receiving an image from an external device via a communication line with a fax, etc. This includes reading out an image stored in a hard disk (including those connected to the computer in addition to those built in the computer). In addition, in a general image processing apparatus such as a digital camera print, the case where an image has already been read from the storage device into the memory and an image on which predetermined image processing has been performed is read from the memory to the image receiving module 110 is also included. . The image is a color image, but the file format (including color space, compression format, etc.) does not matter. In this embodiment, an example in a color space using YCrCb is shown. One image may be received or a plurality of images may be received. Moreover, although the scenery image, such as a photograph, is mainly exemplified as the contents of the image, it may be a document used for business, a pamphlet for advertisement, or the like.

  The noise removal module 120 is connected to the image reception module 110 and the noise addition module 140. The noise of the image received by the image receiving module 110 is removed. Then, the image from which noise has been removed is passed to the noise addition module 140. Specifically, for example, the noise removed by the noise removal module 120 is low-frequency noise.

The threshold matrix generation module 130 is connected to the noise addition module 140. A threshold matrix is generated based on a function that guarantees a minimum in a geometrically isotropic and uniform point distribution. Then, the generated threshold matrix is passed to the noise addition module 140. Here, this function is called an energy function.
For example, when the threshold value matrix generation module 130 determines a point indicating the position of the threshold value, which is data in the threshold value matrix, the threshold value has not yet been determined for a set of points that are equal to or less than the already determined threshold value. With respect to the point 1 and the second point group in the set within a predetermined range from the first point, the value obtained from the first point and the second point group by the energy function is the smallest The point to which the threshold value is given may be determined so that As a specific example, the energy function here is determined by a twice-differentiable function having a predetermined strength convexity.

The noise addition module 140 is connected to the noise removal module 120, the threshold matrix generation module 130, and the image output module 150. The threshold value matrix generated by the threshold value matrix generation module 130 is used to perform mask processing periodically to add noise to the image.
The target image to which the noise adding module 140 adds noise may be either an image received by the image receiving module 110 or an image from which noise has been removed by the noise removing module 120. Then, the image with noise added is passed to the image output module 150.
The noise addition module 140 is either static use using the threshold matrix generated by the threshold matrix generation module 130 or dynamic use using the threshold matrix allocated to a predetermined range. May be switched.
In the case of dynamic use, the maximum value of noise to be superimposed may be controlled by a predetermined range width.
In the case of dynamic use, the width of a predetermined range may be switched for each pixel. As the width of the predetermined range, for example, there is the number of gradations (number of steps) of noise to be added.
The noise addition module 140 may add only the luminance component of the image as a noise addition target.

  The image output module 150 is connected to the noise addition module 140, receives an image to which noise has been added by the noise addition module 140, and outputs the image. To output an image, for example, it is stored in a storage medium such as a memory card, printed by a printing device such as a printer, displayed on a display device such as a display, or transmitted by an image transmission device such as a fax machine. And writing an image to an image storage device such as an image database. In addition, in an integrated image processing apparatus such as a digital camera print, writing an image to which noise has been added by the noise adding module 140 on a memory so as to be processed by subsequent predetermined image processing is also included.

FIG. 2 is a flowchart showing an example of processing according to this embodiment.
In step S202, the image reception module 110 receives an image.
In step S204, the noise removal module 120 removes noise from the image.
In step S206, the threshold matrix generation module 130 generates a threshold matrix used for noise addition. This process will be described later using the flowchart shown in the example of FIG.
In step S208, the noise addition module 140 adds noise to the image using the threshold value matrix generated in step S206.
In step S210, the image output module 150 outputs the image added with noise in step S208.

  FIG. 3 is a flowchart illustrating an example of threshold matrix generation processing by the threshold matrix generation module 130 according to the present embodiment. The threshold matrix generation module 130 creates noise added to the image as a threshold matrix.

  In step S302, a threshold matrix is initialized. A value that cannot be taken as data in the threshold matrix is substituted. For example, when the mask size of the threshold matrix is d × d, the data in the threshold matrix is initialized with d ^ 2 (meaning the square of d).

  As described below, each serial number from 0 to d ^ 2-1 is uniquely given as each data (hereinafter referred to as a pixel) in the threshold matrix, and finally each data value is divided by d ^ 2 / M. For example, a threshold number from 0 to M−1 is assigned to each pixel in the mask to create a threshold matrix. M is the maximum step (number of gradations) of the threshold matrix. A number is repeatedly assigned to each pixel in steps S304 and S306.

In step S304, the number i is assigned to the pixel having the minimum energy function.
Hereinafter, a method for uniquely assigning serial numbers from 0 to d ^ 2-1 to the pixels in the threshold matrix will be described.
When iε {0,..., d ^ 2-1} and serial numbers 0 to i−1 are uniquely assigned to predetermined pixels, respectively, the selection method of the pixel that gives the serial number i can be determined. That's fine.
When determining the serial number i, when all the pixels from the already given serial numbers 0 to i-1 are turned on (when numbers are assigned to the pixels), the highest point among the pixels that are not yet lit. It is necessary to select pixels that can maintain good dispersibility. As an index for this purpose, in this embodiment, the concept of energy is introduced.

ここで、ｆｒは、Ｘに含まれる各点（ｘ、ｙ∈Ｘ）の間で生じる相互エネルギーを意味しており、ｒは、｜ｘ−ｙ｜＞ｒの場合に相互エネルギーが０になることを明示するための添え字である。
次に、総エネルギーＩ（Ｘ，ｆｒ）を、以下の式で定義する。

集合Ｘに対して、総エネルギーを最小化することによって、均一かつ等方な点分散が得られることを保証するためには、関数ｆｒに数学的に保証された条件を与える必要がある。 When X is a point set in the plane, each point energy I (X, x, fr) is defined by the following equation.

Here, fr means the mutual energy generated between each point (x, yεX) included in X, and r becomes 0 when | x−y |> r. This is a subscript to indicate that.
Next, the total energy I (X, fr) is defined by the following equation.

In order to guarantee that uniform and isotropic point dispersion can be obtained by minimizing the total energy for the set X, it is necessary to give a mathematically guaranteed condition to the function fr.

To simplify the notation, the function h defined on the interval [0, 1]
fr (x) = h (x / r) (when x <r),
fr (x) = 0 (when x ≧ r) (Formula 3)
In order to guarantee uniform and isotropic point dispersion, the following three conditions must be imposed.
H1: h is a C2 class monotonically decreasing convex function H2: h (1) = LIM x → 1 h ′ (x) = LIM x → 1 h ″ (x) = 0
H3: (h ″ (x ^ 1/2) / (x ^ 1/2)) ^ (1/2) is a convex function As h satisfying such a condition, for example, h (x) = (2 / 3-x + 1 / 3x ^ 3) ^ 2 (Formula 4)
Is mentioned.

Return to the step of determining the serial number i. If each point energy I (X, x, fr) is used,
X = {all points given serial numbers {0, ..., i-1}}
For x∈ {0,..., D−1} ^ 2-X (points that have not yet been given serial numbers), for a point x that minimizes I (X, x, fr). Then, the serial number i may be given.
In the energy calculation, a periodic boundary condition is provided for the mask. That is, for x, y∈ {0,..., D−1} ^ 2,
| X−y | = min {| xy−ed |: ex, ey = −1, 0, 1} (Formula 5)
Define the distance with.
As fr, the h (x) may be used as it is.

  In step S306, it is determined whether or not the number i has been assigned to all the pixels in the threshold matrix. If it is given (when the threshold matrix is completed), the process ends (step S399), and otherwise, the process returns to step S304.

This completes the process of creating noise to be added to the image as a threshold matrix.
4A shows an example of noise with a high energy value, and FIG. 4B is an explanatory diagram showing an example of noise with a low energy value. In the present embodiment, a state as shown in FIG. 4B is created.
FIG. 5 (a) shows an example of white noise, FIG. 5 (b) shows an example of an energy minimization dispersion pattern, and d = 64, r = 24, and h (x) are expressed by Equation (4). An example of a 64 × 64 threshold matrix created by giving
Note that the process of the flowchart shown in FIG. 3 is only required to be performed before the process by the noise addition module 140, and may be, for example, before step S202. For example, it may be performed in advance independently of the processing by the noise addition module 140, and only the threshold matrix may be stored in the memory.

Next, the process of actually adding noise to the image (processing by the noise adding module 140) will be described.
A description will be given assuming that noise is added to an image of (w, h) size.
The target original image is represented by gv, and the threshold value of the created d × d matrix is represented by th.
At this time, noise is added (superimposed) to the pixel (x, y) on the original image by the following calculation formula.
gv (x, y) = gv (x, y) + p (th (x% d, y% d)) (Formula 6)
However, if gv (x, y) is less than 0, it is clipped (assigned) to 0, and if it exceeds an allowable maximum density (for example, 256), it is clipped (assigned). Note that% is a calculation of the remainder. As a result, the same noise is periodically superimposed on the original image in units of d × d size.

The threshold matrix is called from the memory and used.
Here, the function p is a noise amount control function.
p (i) = − m + (2m + 1) (2th (i) +1) / (2M) (Formula 7)
Can be determined by However, M is the maximum step (number of gradations) of the threshold matrix, and m is the desired maximum noise step.
In this example, each threshold value from 0 to M is converted to a value from -m to m by the function p. If noise is applied to all points of the target original image, the process is terminated. FIG. 6 is an explanatory diagram illustrating an example of threshold matrix allocation processing. For example, the data range 610 (0 to 255) in the threshold matrix 600 having a size of 64 × 64 is allocated to the step allocation range 620 (−m to m).

Hereinafter, a modification etc. are described regarding the process of this Embodiment.
The threshold matrix obtained in the threshold matrix creation step is maximally uniform and equal under the limited condition of the threshold matrix when all pixels having threshold numbers up to a predetermined value are turned on. It is geometrically guaranteed to be a point dispersion.
Compared to the conventional method using the blue noise concept (which guarantees conceptually uniform and isotropic point dispersion), it is possible to obtain not only geometrically correct but also better visual results. .
The created threshold matrix is the same type as the threshold matrix used in image binarization (halftone) processing, and can be used as it is for halftone.
For example, if M = 256, a threshold matrix of 256 gradations is obtained.

In creating the threshold matrix, in the case of a conventional method such as a blue noise mask, when a simple method is adopted in which the i-th is always determined from 0 to i-1 as in the present embodiment, There is a tendency for the roughness of the pattern in the matrix to increase.
In that sense, it can be said that the method of determining the i-th from 0 to i−1 in the present embodiment is made possible only by the energy function of the present embodiment.

As an example of a suitable parameter in the present embodiment,
d = 64,
M = 256,
m = -4 (Formula 8)
Is mentioned.
In general, the blue noise mask cannot substantially eliminate periodic noise in the 64 × 64 size, but in the case of the threshold matrix according to the present embodiment, there is almost no fear of periodic noise, and the size is smaller from the viewpoint of memory. High image quality can be achieved.

Regarding the function p, since the threshold value matrix is periodically used, th (x, y) = p (th (x, y)) (Formula 9) before the noise applying process
Put it on again,
gv (x, y) = gv (x, y) + th (x% d, y% d) (Formula 10)
You may make it process by this, and it can speed up by doing in this way.
In addition, when m is used in a fixed manner from the beginning, the threshold matrix itself may be replaced with p (th (x, y)) and stored.
As another example of the function p, there is a method of changing the calculation method according to the pixel (x, y). In this case, for example, gv (x, y) = gv (x, y) + p (x, y, th (x% d, y% d)) (Formula 11)
p (x, y, i) = − m (x, y) + (2m (x, y) +1) (2th (i) +1) / (2M) (Formula 12)
The value of m may be a function of the pixel (x, y).
Examples of m (x, y) include a function that increases when the pixel (x, y) is a flat part (part where the density change is slow) and decreases when the pixel (x, y) is a change part (part where the density change is abrupt) , A function that increases when the pixel (x, y) is a strong blue portion, a function that decreases when the pixel (x, y) is a strong red portion, and a function that combines both of them.
In addition, according to the function that combines both of these, the noise added in the gradation area such as the blue sky in the image is increased, the gradation step is reduced, and the noise is not added in other parts. .

As described above, in the present embodiment, for example, a strong reddish portion such as skin color may be excluded in advance, and noise may be applied only to a specific gradation region such as a blue sky. However, in the present embodiment, since the configuration is such that noise with less roughness is originally placed, gradation steps can be suppressed without defects even without such a complicated configuration.
Furthermore, as described above, the noise removal technology and the noise addition technology have contradictory aspects. However, according to the present embodiment, noise removal is performed according to the present embodiment after removing unnecessary noise from the image in advance. By adopting a configuration for performing processing, both noise and gradation steps can be suppressed.
In addition, since the gradation step has a large part due to the luminance change, it is only necessary to apply noise to only the Y component indicating the luminance among the YCbCr components of the image, and the execution can be performed faster than applying noise to all components It becomes.

  FIG. 7 is an explanatory diagram illustrating a processing example according to the present exemplary embodiment. The example illustrated in FIG. 7A is an original image to be received by the image receiving module 110. The example shown in FIG. 7B is an image obtained by removing noise from the original image by the noise removing module 120. The example illustrated in FIG. 7C is an image in which noise is added from the image from which noise is removed by the noise addition module 140. From this experimental result example, it can be seen that the roughness of the original image is reduced, the tone jump is suppressed, and the roughness / periodic pattern is reduced by noise superposition.

  A hardware configuration example of the image processing apparatus according to the present embodiment will be described with reference to FIG. The configuration shown in FIG. 8 is configured by a personal computer (PC), for example, and shows a hardware configuration example including a data reading unit 817 such as a scanner and a data output unit 818 such as a printer.

  A CPU (Central Processing Unit) 801 is a computer that describes execution sequences of various modules described in the above-described embodiments, that is, the noise removal module 120, the threshold matrix generation module 130, the noise addition module 140, and the like. It is a control part which performs the process according to a program.

  A ROM (Read Only Memory) 802 stores programs used by the CPU 801, operation parameters, and the like. A RAM (Random Access Memory) 803 stores programs used in the execution of the CPU 801, parameters that change as appropriate during the execution, and the like. These are connected to each other via a host bus 804 including a CPU bus.

  The host bus 804 is connected to an external bus 806 such as a PCI (Peripheral Component Interconnect / Interface) bus via a bridge 805.

  A keyboard 808 and a pointing device 809 such as a mouse are input devices operated by an operator. The display 810 includes a liquid crystal display device or a CRT (Cathode Ray Tube), and displays various types of information as text or image information.

  An HDD (Hard Disk Drive) 811 includes a hard disk, drives the hard disk, and records or reproduces a program executed by the CPU 801 and information. The hard disk stores received images and processed images. Further, various computer programs such as various other data processing programs are stored.

  The drive 812 reads out data or a program recorded in a removable recording medium 813 such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, and reads the data or program as an interface 807 or an external bus 806. , And supplied to the RAM 803 connected via the bridge 805 and the host bus 804. The removable recording medium 813 can also be used as a data recording area similar to a hard disk.

  The connection port 814 is a port for connecting the external connection device 815, and has a connection unit such as USB or IEEE1394. The connection port 814 is connected to the CPU 801 and the like via the interface 807, the external bus 806, the bridge 805, the host bus 804, and the like. The communication unit 816 is connected to a network and executes data communication processing with the outside. The data reading unit 817 is a scanner, for example, and executes document reading processing. The data output unit 818 is, for example, a printer, and executes document data output processing.

  Note that the hardware configuration of the image processing apparatus illustrated in FIG. 8 is an example of the configuration, and the present embodiment is not limited to the configuration illustrated in FIG. 8, and the modules described in the present embodiment are executed. Any configuration is possible. For example, some modules may be configured with dedicated hardware (for example, Application Specific Integrated Circuit (ASIC), etc.), and some modules are in an external system and connected via a communication line In addition, a plurality of systems shown in FIG. 8 may be connected to each other via communication lines so as to cooperate with each other. Even if it is incorporated in a digital camera, copying machine, fax machine, scanner, printer, multifunction device (an image processing apparatus having at least two functions of a scanner, printer, copying machine, fax machine, etc.) Good.

The program described above may be provided by being stored in a recording medium, or the program may be provided by communication means. In that case, for example, the above-described program may be regarded as an invention of a “computer-readable recording medium recording the program”.
The “computer-readable recording medium on which a program is recorded” refers to a computer-readable recording medium on which a program is recorded, which is used for program installation, execution, program distribution, and the like.
The recording medium is, for example, a digital versatile disc (DVD), which is a standard established by the DVD Forum, such as “DVD-R, DVD-RW, DVD-RAM,” and DVD + RW. Standard “DVD + R, DVD + RW, etc.”, compact disc (CD), read-only memory (CD-ROM), CD recordable (CD-R), CD rewritable (CD-RW), Blu-ray disc ( Blu-ray Disc (registered trademark), magneto-optical disk (MO), flexible disk (FD), magnetic tape, hard disk, read-only memory (ROM), electrically erasable and rewritable read-only memory (EEPROM), flash Includes memory, random access memory (RAM), etc. .
The program or a part of the program may be recorded on the recording medium for storage or distribution. Also, by communication, for example, a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a wired network used for the Internet, an intranet, an extranet, etc., or wireless communication It may be transmitted using a transmission medium such as a network or a combination of these, or may be carried on a carrier wave.
Furthermore, the program may be a part of another program, or may be recorded on a recording medium together with a separate program. Moreover, it may be divided and recorded on a plurality of recording media. Further, it may be recorded in any manner as long as it can be restored, such as compression or encryption.

DESCRIPTION OF SYMBOLS 110 ... Image reception module 120 ... Noise removal module 130 ... Threshold matrix generation module 140 ... Noise addition module 150 ... Image output module

Claims (7)

Image receiving means for receiving images;
Matrix generating means for generating a matrix based on a function that guarantees a minimum value in a geometrically isotropic and uniform point distribution;
Noise adding means for periodically adding a noise to the image received by the image receiving means by performing mask processing using the matrix generated by the matrix generating means ,
In the first case where the noise addition means statically uses the matrix generated by the matrix generation means, or in the second case where the matrix is dynamically allocated within a predetermined range and used. In the second case, the width of the predetermined range is switched for each pixel, and the maximum value of noise to be superimposed is controlled by the width of the predetermined range. An image processing apparatus. When determining the point indicating the position of the threshold value which is data in the matrix, the matrix generating means targets a set of points below the already determined threshold value and the first point for which the threshold value has not yet been determined. The value obtained by the function from the first point and the second point group is minimized with respect to the second point group in the set within a predetermined range from the first point. The image processing apparatus according to claim 1, wherein a point to which the threshold value is given is determined. The image processing apparatus according to claim 2, wherein the function is determined by a twice differentiable function having a predetermined strength convexity. Noise removing means for removing noise of the image received by the image receiving means,
The image processing apparatus according to any one of claims 1 to 3 , wherein the noise adding unit adds noise to an image from which noise has been removed by the noise removing unit. The noise adding means includes
The image processing apparatus according to any one of claims 1 to 4 , wherein noise is added to a luminance component of an image. The noise adding means includes
The image processing apparatus according to any one of claims 1 to 5 , wherein noise is added to a gradation area in the image. Computer
Image receiving means for receiving images;
Matrix generating means for generating a matrix based on a function that guarantees a minimum value in a geometrically isotropic and uniform point distribution;
Periodically functioning as a noise adding means for adding noise to the image received by the image receiving means by performing mask processing using the matrix generated by the matrix generating means ,
In the first case where the noise addition means statically uses the matrix generated by the matrix generation means, or in the second case where the matrix is dynamically allocated within a predetermined range and used. In the second case, the width of the predetermined range is switched for each pixel, and the maximum value of noise to be superimposed is controlled by the width of the predetermined range. An image processing program characterized by the above.

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