JPH07184050A - Image processor - Google Patents

Abstract

PURPOSE:To provide an image processor by which an excellent multilevel image without production of moire or texture in a dot image or a photographic image. CONSTITUTION:Received image data are subject to filter processing such as MTF correction by a filter circuit 203 and processed by n-value processing at an n-value processing circuit 204 and the result is given to a computer 205. On the other hand, random number data generated from a random number generator 201 are inputted to the computer 205 as a noise signal via a coded m-value processing circuit 202. The computer 205 adds the noise signal to the picture data. The image data with the noise signal added to them are fed to a printer 206, which prints out the data on output paper.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing apparatus, and more particularly to an image processing apparatus for performing pseudo halftone processing on an input image.

[0002]

2. Description of the Related Art Conventionally, in an image processing apparatus such as a digital copying machine or a facsimile, pseudo halftone processing is performed in order to improve gradation reproducibility of photographic originals and halftone dot originals. The systematic dither method and the error diffusion method are known as pseudo halftone processing methods for these conventional image processing apparatuses.
In the error diffusion method, the difference between the input pixel data and the output pixel data, that is, the error is diffused and added to surrounding pixels with a predetermined weighting.

As a method similar to the error diffusion method, there is known a method of adding recording energy other than the error as described in, for example, Japanese Patent Laid-Open No. 3-220867. This is to add the recording energy so that the gradation between the pixels changes smoothly by referring to the recording energy of the pixels around the pixel of interest.

[0004]

The above-mentioned prior art has the following problems. In the systematic dither method, when a printed halftone original is processed, moire and the like occur due to the interference of the cycles of the halftone dots and the dither matrix, resulting in significant image quality deterioration. On the other hand, in the error diffusion method, when a photograph (continuous tone) original is processed, a unique texture occurs,
After all, the image quality deteriorates. Further, also in the method of adding the recording energy other than the error, the image quality is deteriorated similarly to the error diffusion.

[0005]

SUMMARY OF THE INVENTION The present invention has been made in order to solve the above-mentioned problems of the prior art. In an image processing apparatus for applying pseudo-halftone processing to input image data and outputting it, noise signals Noise signal generating means for generating a random number, random number generating means for generating a random number, and noise signal adding means for adding the noise signal generated by the noise signal generating means to the input image data. A configuration is adopted in which a noise signal is added to the input image data based on the random number generated by the random number generating means.

Further, in an image processing apparatus for applying pseudo halftone processing to input image data and outputting the same, a noise signal generating means for generating a noise signal, a random number generating means for generating a random number, and the noise signal generating means are provided. Noise signal adding means for adding or subtracting the generated noise signal to the input image data, and processing determining means for determining whether the noise adding means adds, subtracts, or does not add and subtract the noise signal to the input image data. And the process determining means employs a configuration for determining the process based on a random number generated by the random number generating means.

Further, the noise signal generating means employs a structure for generating a noise signal having a magnitude corresponding to the output image density. The noise signal generating means employs a configuration for generating a noise signal of a fixed magnitude. The noise signal generating means employs a configuration that generates a noise signal that corresponds to the random number generated by the random number generator and has a magnitude equal to or less than a value that corresponds to the output image density. The noise signal generating means employs a configuration that generates a noise signal having a magnitude greater than a value corresponding to the random number generated by the random number generator and corresponding to the output image density.

Further, the noise signal generating means adopts a structure which corresponds to the random number generated by the random number generator and generates a noise signal having a magnitude equal to or smaller than an arbitrarily determined value. The noise signal generating means adopts a configuration that corresponds to the random number generated by the random number generator and generates a noise signal having a magnitude equal to or larger than an arbitrarily determined value. The noise signal adding means adopts a configuration in which the noise signal is added only to an arbitrary density portion of the input image data.

According to the present invention, when the pseudo halftone processing is performed on the input image data, noise is added to the image data by using a random number. Also, it is possible to prevent the generation of moire that deteriorates the image, and to perform smooth gradation reproduction.

[0009]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments of the present invention will be specifically described below with reference to the accompanying drawings. [First Embodiment] As a first embodiment, a digital copying machine using the present invention will be described. In the first embodiment, a noise signal is added to output pixel data based on a random number.

FIG. 1 shows the configuration of a digital copying machine to which the present invention is applied. The scanner 101 scans and reads an original image (not shown), and the read image data is digitized by A / D conversion and input to the γ correction circuit 102.
The γ correction circuit 102 performs correction so that the image data output from the scanner 101 has an appropriate γ characteristic.

The image data that has been gamma corrected by the gamma correction circuit 102 is sent to a filter circuit 103. The filter circuit 103 performs processing such as correction of MTF deteriorated by the image reading operation of the scanner 101, smoothing for reducing sampling moire, and the like.

The image data processed by the filter circuit 103 is sent to the pseudo halftone processing circuit 104. The pseudo halftone processing circuit 104 converts the image data according to the number of output gradations of the printer by a method described later, and outputs the processed data to the printer 105. The printer 105 records the sent image data on an output sheet and outputs it.

Next, the pseudo halftone circuit 104 will be described in detail. FIG. 2 shows the configuration of the pseudo halftone circuit 104.
The image data output from the filter circuit 203 is input to the n-valued circuit 204 and is n-valued according to the number of gradations of the printer. The number of gradations referred to here is the number of gradations that can be expressed for one pixel. The n-valued image data is input to the computer 205.

On the other hand, the random number generator 201 generates random numbers. The generated random number is input to the signed m-ary conversion circuit 202, converted into a noise signal with a signed 2m + 1 value (n> m) of −m to + m, and then input to the computer 204.

The computer 204 adds a 2m + 1-valued noise signal to the n-valued image data. The image data to which the noise signal is added is output to the printer 206, and is copied and output by the printer 206.

FIG. 3 shows an example of noise signal characteristics added. Since the random number outputs a value of -1 to +1, the addition amount can be randomly determined by the product with the arbitrary constant α.

When the image density data before adding noise is Din, the pixel data to be output is Dout, the constant is α, and the returned random number is rand, the equation (1) is established. Dout = Din + α × rand (1) (-1 ≦ rand ≦ + 1)

[Second Embodiment] Next, a second embodiment will be described. The pseudo halftone processing circuit 104 in FIG.
Other than that, the configuration is the same as that of the device of the first embodiment, and the description thereof is omitted. In the second embodiment, random numbers are used to determine whether to add, reduce or not add a noise signal of a certain size.

FIG. 4 shows the configuration of the pseudo halftone processing circuit of the second embodiment. The flow of image data is the same as that in the first embodiment, and will not be described. The process decision circuit 405 has
The noise signal output from the noise generator 401 and the random number output from the random number generator 402 are input. The process determination circuit 405 adds the noise signal output from the noise generator 40 to the process of the input image data by the random number output from the random number generator 402, or
Randomly determine whether to subtract or not to add or subtract. The computer 406 processes the image data according to the processing content determined by the processing determination circuit 405.

The noise generated by the noise generator 401 may be a constant as shown in FIG. Alternatively, the noise generator 401 and the random number generator 402 may be commonly used and random numbers as shown in FIG. 11 may be used.

When the magnitude of the noise signal to be generated is α and the random number is rand (−1 ≦ rand ≦ + 1), α × rand is −
Take one of the three values α, + α, or 0. For example, a method of selecting one of the three values by the remainder of α × rand is used. When the added noise is Rout, the following expression (2) is established. Dout = Din + Rout (2)

When the generated noise signal itself is a random number, Rout is set to take -α × rand, + α × rand or 0. For example, a method of selecting one of the three values by the remainder of α × rand is used. In this case, a noise signal is not given (Rou
t = 0) The frequency can also be increased.

[Third Embodiment] Next, a third embodiment will be described. The pseudo halftone processing circuit 104 in FIG.
Other than that, the configuration is the same as that of the device of the first embodiment, and the description thereof is omitted. In the third embodiment, the magnitude of the added noise signal is a function of the output image density. FIG. 6 shows a pseudo halftone processing circuit of this embodiment.

The random number generated by the random number generator 601 is input to the noise amount calculation circuit 602. On the other hand, the filter circuit 604 calculates the output image density from the input image data, and this output image signal is input to the noise amount calculation circuit 602. In the noise amount calculation circuit 602, the function f (x)
The magnitude of the noise signal to be added is determined by the product of the value of (x = image density before addition of noise signal) and the random numbers −1 to +1 from the random number generator 601.

FIG. 7 shows an example of the relationship between the output image data, the function f (x) and the noise amount. Assuming that the random number from the random number generator 601 is rand (−1 ≦ rand ≦ + 1), the point represented by □ in the figure has a value f (x) within the range of the function f (x) or less.
Xrand. Density data before adding noise signal (Din)
The relationship between the output data and is expressed by equation (3). Dout = Din + f (Din) × rand (3) (−1 ≦ rand ≦ + 1)

[Fourth Embodiment] Next, a fourth embodiment will be described. The pseudo halftone processing circuit 104 in FIG.
Other than that, the configuration is the same as that of the device of the first embodiment, and the description thereof is omitted. In the fourth embodiment, the size of the noise signal to be added is a function of the output image density, and it is determined by using a random number whether to add, subtract, or neither add nor subtract a noise signal of a certain size. Is.

The configuration of the halftone processing circuit of the fourth embodiment is shown in FIG.
Shown in. The process determining circuit 803 determines whether to add, subtract, or not add or subtract a noise signal of a certain magnitude obtained from the function f (x) in the noise calculating circuit 801 to the random number generator 802. It is randomly determined by the random number from. The computer 806 is a processing decision circuit 803.
The image data is processed according to the determination of.

The noise magnitude and the function f are shown in FIGS.
The relationship of (x) is shown. The magnitude of the noise signal is determined by a function of image density. If the random number from the random number generator 802 is rand (−1 ≦ rand ≦ + 1) and the magnitude of noise to be generated is calculated by f (Din), then f (Din) ×
rand is set to select one of the three values of -f (Din), + f (Din) or 0. For example, f (D
(in) × rand is set to select one of the three values by the remainder of 3. If the noise signal is Rf (out),
The following expression (4) is established. Dout = Din + Rf (out) (4)

As shown in FIGS. 7 and 11, if the generated noise itself is a random number, Rf (out) is -f (Din).
It may be set to be × rand, + f (Din) × rand or 0. For example, depending on the remainder of the value of f (Din) × rand, either addition, subtraction, or neither addition nor subtraction is randomly determined. In this case, the frequency of not giving a noise signal (Rout = 0) can be increased.

[Fifth Embodiment] Next, a fifth embodiment will be described. The pseudo halftone processing circuit 104 in FIG.
Other than that, the configuration is the same as that of the device of the first embodiment, and the description thereof is omitted. In the fifth embodiment, the magnitude of the noise signal is set to a certain level or more. FIG. 12 shows an example of the relationship between the image density and the size of the noise signal. By setting the generation of the random number to be α or more and β or less, the addition amount of the noise signal can be determined in an arbitrary range. If the magnitude of noise to be generated is Rout, then equation (5) holds. Rout = (α + β) / 2 + ((α−β) / 2) × ran
d …… (5) (-1 ≦ rand ≦ + 1)

[Sixth Embodiment] Next, a sixth embodiment will be described. Since the configuration other than the pseudo halftone processing circuit 104 in FIG. 1 is the same as that of the apparatus of the first embodiment, the description thereof will be omitted. In the sixth embodiment, the magnitude of the noise signal is set to be equal to or larger than the value taken by the image density function. FIG. 13 shows an example of the relationship between the image density and the size of the noise signal. By setting the random number generation to f (x) or more and α or less, it is possible to determine the addition amount of the noise signal in an arbitrary range. When the magnitude of noise to be generated is Rout, the equation (6) holds. Rout = (α + f (x)) / 2 + ((α-f
(X)) / 2) × rand …… (6)

[Seventh Embodiment] Next, a seventh embodiment will be described. The pseudo halftone processing circuit 104 in FIG.
Other than that, the configuration is the same as that of the device of the first embodiment, and the description thereof is omitted. In the seventh embodiment, the smaller the image density, the larger the noise signal to be added.
FIG. 10 shows an example in which the function value of the image density is used as the magnitude of the noise signal. In this example, the function f (x) is inversely proportional to the image density. Further, FIG. 11 is an example in which the magnitude of the noise signal is equal to or smaller than the function value of the image density. In this example, the value obtained by the random number has a wider range of values as the density is lower, and the influence of the addition of the random number becomes greater.

[Eighth Embodiment] Next, an eighth embodiment will be described. The pseudo halftone processing circuit 104 in FIG.
Other than that, the configuration is the same as that of the device of the first embodiment, and the description thereof is omitted. The eighth embodiment adds a noise signal only to an arbitrary gradation part. FIG. 14 shows the relationship between the image density and the added noise signal. In this case, a noise signal of a certain amount α is generated in the low density portion (<x). In addition to this, it is also possible to take a function value of the image density up to the image density x and not add it at the image density x or higher. Also,
It is also possible to add a noise signal obtained by a random number only to a certain fixed density portion.

[Ninth Embodiment] Next, a ninth embodiment will be described. The pseudo halftone processing circuit 104 in FIG.
Other than that, the configuration is the same as that of the device of the first embodiment, and the description thereof is omitted. The ninth embodiment adds noise having a high level in a high frequency region, instead of white noise as a noise signal to be added. FIG. 15 shows the white noise, and FIG. 16 shows the noise level with high level in the high frequency region. FIG. 17
In order to generate high level noise in the high frequency region, we show a configuration to perform high pass filtering on the output data of the random number generator.

The data output from the random number generator 1701 passes through the high-pass filter circuit 1702, so that only high-frequency data is input to the signed m-value conversion circuit 1703.

[0036]

As described above, according to the present invention, in the image processing apparatus for applying the pseudo halftone processing to the input image data and outputting it, the noise signal generating means for generating the noise signal and the random number generation are performed. Random number generating means and noise signal adding means for adding the noise signal generated by the noise signal generating means to the input image data, and the noise signal adding means generates noise based on the random number generated by the random number generating means. Since the configuration in which the signal is added to the input image data is adopted, it is possible to perform smooth gradation reproduction without generating moire or the like.

Further, in the image processing apparatus for applying the pseudo halftone processing to the input image data and outputting it, the noise signal generating means for generating a noise signal, the random number generating means for generating a random number, and the noise signal generating means are provided. Noise signal adding means for adding or subtracting the generated noise signal to the input image data, and processing determining means for determining whether the noise adding means adds, subtracts, or does not add and subtract the noise signal to the input image data. Since the processing determining means has a configuration for determining the processing based on the random number generated by the random number generating means, it is possible to obtain a good image output without moiré or the like with simple hardware.

Further, since the noise signal generating means adopts a structure for generating a noise signal having a magnitude corresponding to the output image density, the addition amount of the noise signal can be effectively set and a better image output can be obtained. be able to. Since the noise signal generating means adopts a configuration for generating a noise signal of a constant magnitude, it is possible to add a noise signal of a proper magnitude according to the hardware of the printer or the like.

Further, since the noise signal generating means adopts a structure for generating a noise signal having a magnitude equal to or smaller than a value corresponding to the random number generated by the random number generator and corresponding to the output image density, there is no moire. Good image output can be obtained. Since the noise signal generating means adopts a configuration for generating a noise signal having a magnitude corresponding to the random number generated by the random number generator and corresponding to the output image density, the same effect can be obtained.

Since the noise signal generating means adopts a structure which corresponds to the random number generated by the random number generator and generates a noise signal having a magnitude equal to or smaller than an arbitrarily determined value, the same effect can be obtained. The noise signal generating means has a similar effect because it adopts a configuration that corresponds to the random number generated by the random number generator and generates a noise signal having a magnitude larger than an arbitrarily determined value.

Since the noise signal adding means adopts a structure in which the noise signal is added only to an arbitrary density portion of the input image data, the noise signal can be added only to a necessary portion, so that there is no gradation skip. Smooth gradation reproduction is possible.

[Brief description of drawings]

FIG. 1 is a block diagram of a digital copying machine according to an embodiment.

FIG. 2 is a configuration diagram of a pseudo halftone processing circuit according to the first embodiment.

FIG. 3 is an explanatory diagram of a noise signal.

FIG. 4 is a configuration diagram of a pseudo halftone processing circuit according to a second embodiment.

FIG. 5 is an explanatory diagram of a noise signal.

FIG. 6 is a configuration diagram of a pseudo halftone processing circuit according to a third embodiment.

FIG. 7 is an explanatory diagram showing an addition amount of a noise signal.

FIG. 8 is a configuration diagram of a pseudo halftone processing circuit according to a fourth embodiment.

FIG. 9 is an explanatory diagram showing an addition amount of a noise signal.

FIG. 10 is an explanatory diagram showing an addition amount of a noise signal.

FIG. 11 is an explanatory diagram showing an addition amount of a noise signal.

FIG. 12 is an explanatory diagram showing an addition amount of a noise signal.

FIG. 13 is an explanatory diagram showing an addition amount of a noise signal.

FIG. 14 is an explanatory diagram showing an addition amount of a noise signal.

FIG. 15 is a diagram showing frequency characteristics of a noise signal to be added.

FIG. 16 is a diagram showing frequency characteristics of a noise signal to be added.

FIG. 17 is a configuration diagram of a pseudo halftone processing circuit according to a ninth embodiment.

[Explanation of symbols]

 101 Scanner 102 γ Correction Circuit 103 Filter Circuit 104 Pseudo Halftone Processing Circuit 105 Printer

Claims (9)

[Claims] 1. An image processing apparatus for applying pseudo-halftone processing to input image data and outputting the noise signal, the noise signal generating means for generating a noise signal, the random number generating means for generating a random number, and the noise signal generating means. A noise signal adding means for adding the generated noise signal to the input image data, wherein the noise signal adding means adds the noise signal to the input image data based on the random number generated by the random number generating means. Image processing device. 2. An image processing apparatus for applying pseudo halftone processing to input image data and outputting the same, comprising: a noise signal generating means for generating a noise signal; a random number generating means for generating a random number; and the noise signal generating means. Noise signal adding means for adding or subtracting a generated noise signal to input image data, and processing determining means for determining whether the noise adding means adds, subtracts, or does not add and subtract a noise signal to the input image data And an image processing apparatus, wherein the process determining means determines a process based on a random number generated by the random number generating means. 3. The image processing apparatus according to claim 1,
An image processing apparatus, wherein the noise signal generating means generates a noise signal having a magnitude corresponding to an output image density. 4. The image processing apparatus according to claim 2,
An image processing apparatus, wherein the noise signal generating means generates a noise signal of a constant magnitude. 5. The image processing apparatus according to claim 1 or 2, wherein the noise signal generating means corresponds to a random number generated by the random number generator and has a magnitude equal to or less than a value corresponding to an output image density. An image processing apparatus, wherein: 6. The image processing apparatus according to claim 1 or 2, wherein the noise signal generating means corresponds to a random number generated by the random number generator and has a noise signal having a magnitude greater than a value corresponding to an output image density. An image processing apparatus, wherein: 7. The image processing apparatus according to claim 1 or 2, wherein the noise signal generating means generates a noise signal corresponding to a random number generated by the random number generator and having a magnitude equal to or smaller than an arbitrarily determined value. An image processing device characterized by: 8. The image processing apparatus according to claim 1 or 2, wherein the noise signal generating means generates a noise signal corresponding to a random number generated by the random number generator and having a magnitude larger than an arbitrarily determined value. An image processing device characterized by: 9. The image processing apparatus according to claim 1, wherein the noise signal adding means adds a noise signal only to an arbitrary density portion of input image data.