JPH10283460A - Image processor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To easily obtain a desired image quality by extracting the characteristic amount of image data to be inputted and selecting an appropriate processing part that corresponds to the characteristic amount among plural kinds of processing parts. SOLUTION: An image processing unit 115 consists of an A/D converting part 31, a characteristic amount extracting part 32 which operates as a characteristic amount extracting means, an image processing selecting part 33 which works as an image processing selecting means, a learning part 34 which serves as a learning means and an image processing part 35. When an operator sets an original that is wanted to copy by him on an image reader, a characteristic amount DH is extracted from image data D1 that is acquired by reading the original. Selection data DS is outputted from the part 33 in accordance with the amount DH, and an appropriate processing part is selected from the part 35 according to the data DS. The data D1 is processed through a selected processing part and outputted as image data DO. Printing is performed on paper based on the outputted data DO.

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 for performing image processing of contents corresponding to characteristics of input image data and outputting the processed image data. A feature of the present invention is that a feature amount of image data is used as an input parameter to a neural network, and an optimal one is selected from a plurality of image processing means (image parameters) prepared in advance by selection data output from the neural network. Is to do. The image processing apparatus of the present invention, for example, in a digital copying machine,
It is used for image quality correction for controlling sharpness of image quality for image data input by reading a document.

[0002]

2. Description of the Related Art Conventionally, neural networks are often used for image processing. For example, JP-A-6-3
Japanese Patent Application Publication No. 0253 proposes a device for inputting input image data to an input layer of a neural network, performing image processing by the neural network, and outputting the processed image data from an output layer.

It has also been proposed to input a feature amount of image data to a neural network, obtain a conversion parameter corresponding to the feature amount by the neural network, and perform image processing using the obtained conversion parameter.

[0004] In each of these, image processing is performed by the neural network itself, and the processed image data is output from the neural network.

[0005]

In the above-mentioned conventional image processing apparatus, since the image processing itself is performed by a neural network, it is necessary to increase the number of each layer of the neural network, especially the number of output layers. As the circuit scale of the network becomes enormous, enormous time is required for learning.

For example, when processing image data of 256 tones, at least about one fifth of the number of neurons is required in the output layer, and 256 neurons are required to perform high-precision processing. is required. Such a neural network has a huge circuit scale and a large learning time.

[0007] Since image processing is performed by a neural network after learning, there is a possibility that image processing that cannot be normally performed depending on a document may be performed. That is,
If teacher data for learning is not appropriate or learning is not sufficient, there is a possibility that the neural network will execute image processing with abnormal contents that cannot be considered normally depending on input image data.

Further, it is conceivable to select an appropriate one from a plurality of image processing means set in advance according to a feature amount included in image data without using a neural network. In this case, the problem is how to select an appropriate one.

That is, when selecting the image processing means,
For example, it is important to set parameters such as threshold values for various originals copied by a copying machine so as to accurately determine the type of image. Some parameters such as the threshold value can be calculated, but in practice, they are often obtained empirically using several types of documents. Therefore, it may not be possible to select an appropriate image processing for the document actually used with the obtained threshold value. Therefore, an appropriate image processing means may be selected for image data of a limited document, but an inappropriate image processing means may be selected for another image data. That is, there is a possibility that general-purpose image data such as a document copied by a copying machine cannot be used. In addition, in order to set the parameters, specialized knowledge about image characteristics and image processing is required, and there is a problem that a user without specialized knowledge cannot set parameters.

The present invention has been made in view of the above problems, and uses a relatively small-scale neural network.
It is possible to reduce the time required for learning, without generating abnormal image processing means (image parameters) for a general-purpose document, and selecting an appropriate one from a plurality of prepared image processing means. It is an object of the present invention to provide an image processing apparatus capable of easily obtaining a desired image quality by selecting the image processing apparatus.

[0011]

According to a first aspect of the present invention, there is provided an image processing apparatus for performing, on input image data, image processing of contents corresponding to characteristics of the image data, and outputting the processed image data. An image processing unit having a plurality of types of image processing units having different image processing contents; a feature amount extracting unit for extracting a feature amount of the input image data; And a neural network for selecting an appropriate image processing means corresponding to the quantity.

According to a second aspect of the present invention, there is provided an image processing section having a plurality of types of image processing means having different image processing contents, a feature amount extracting means for extracting a feature amount of the input image data, A neural network that is input with a feature amount to the input layer and is trained to output selection data for selecting an image processing unit corresponding to the input feature amount from an output layer; and the plurality of types of images by the neural network. Image processing selecting means for selecting an appropriate image processing means corresponding to the feature value extracted by the feature value extracting means from the processing means.

According to a third aspect of the present invention, there is provided an image processing section having a plurality of types of image processing means having different image processing contents, a feature amount extracting means for extracting a feature amount of the input image data, A feature amount extracted by the feature amount extraction means is input to an input layer, and a neural network which outputs selection data for selecting one image processing means from the plurality of types of image processing means from an output layer; Learning means for inputting a feature value to a layer and learning the neural network so as to output selection data for selecting an appropriate image processing means corresponding to the input feature value from the output layer. It is composed.

According to a fourth aspect of the present invention, there is provided an apparatus wherein the plurality of types of image processing means include image parameters for outputting image data of density values corresponding to the density values of input image data. You.

In the image processing apparatus of the present invention, for example, image processing means (processing section) having various input / output characteristics as shown in FIG. 5 is prepared in advance. The feature amount extracted from the image data is input to the neural network, and selection data for selecting an appropriate image processing means is output from the output layer of the neural network.

That is, the image processing itself is executed by image processing means prepared in advance. The neural network is used to select any image processing means.

In order for the neural network to select an appropriate image processing means, learning is performed by various methods using teacher data. Such learning is
This can be performed in advance, or can be performed when the image processing apparatus is actually used. Note that the feature amount includes the image data itself. That is, the image data itself may be input to the neural network.

[0018]

FIG. 1 is a diagram showing a schematic configuration of a digital copying machine 1 provided with an image processing unit 115 according to the present invention.

The copying machine 1 has an image reading device 11 installed at an upper part thereof, and an image recording device 1 installed at a lower part thereof.
2 is comprised. In the image reading device 11, the original is irradiated with light from the light source 111 using the reduction optical system, and the reflected light is imaged on the linear CCD 114 via the mirror 112 and the lens 113, and the image is photoelectrically converted. An analog electric signal S1 is obtained. In the present embodiment, the resolution of the CCD 114 is 400 dpi, and the maximum document size is A3 size (about 5000 dots per line).
t).

The electric signal S1 output from the CCD 114
Is an image processing unit 1 as an image processing apparatus of the present invention.
15 in the image processing unit 115 according to the contents of the instruction data DC from the operation unit 116.
The image data is converted into digital image data D1 and subjected to image processing such as scaling and image quality correction.
Is output from the image reading device 11.

The original is scanned in the main scanning direction by the CCD 114, and is scanned in the sub-scanning direction by moving the mirror 112 in the horizontal direction. Therefore, the electric signal S1
Are sequentially transferred and output from the CCD 114 for each line of the main scanning.

In the image recording device 12, the image data DO output from the image reading device 11 is converted into an analog electric signal S2 by a laser diode driving unit 121.
Into a laser diode 12 which is a laser emission source.
2. The laser light is converted to laser light by the
3 to form an image on the surface of the photosensitive drum 124. In other words, the current input to the laser diode 122 is controlled in pixel units to control the amount of light, thereby controlling the amount of toner adhering to the surface of the photoconductor drum 124. Is reproduced on the paper PP.

FIG. 2 is a block diagram showing the overall configuration of the image processing unit 115. Image processing unit 115
Are transmitted from an A / D conversion unit 31, a feature amount extraction unit 32 as a feature amount extraction unit in the present invention, an image processing selection unit 33 as an image processing selection unit, a learning unit 34 as a learning unit, and an image processing unit 35. It is configured.

The A / D converter 31 converts the electric signal S1 output from the CCD 114 into 8-bit (256 gradations) digital image data D1 (V7-0). The feature amount extraction unit 32 is configured to output the image data D1
A feature value DH representing the characteristics of the image is extracted. The feature amount extraction unit 32 may extract a plurality of types of feature amounts DH for one type of image data D1. The feature amount includes, for example, edge information, isolated point information, histogram information, and the like, which will be described later. Note that the image data D1 itself may be used as the feature value DH.

Examples of the characteristics of an image include that the image is a character image, a photographic image (shade image), or a halftone image. That is, in these cases, based on the feature amount DH, optimal processing is performed corresponding to each type of image such as a character image, a photographic image, or a halftone image.

The image processing selecting section 33 outputs selection data DS for selecting an optimum processing for the image processing of the image data D1 based on the characteristic amount DH. That is, the image processing itself on the image data D1 is performed by the image processing unit 35, but the image processing selection unit 33 outputs selection data DS for selecting the content of the processing performed in the image processing unit 35. The image processing selection unit 33 is configured by a neural network.

The learning section 34 is for learning the neural network of the image processing selecting section 33. That is, the learning unit 34 receives the selection data DS output from the image processing selection unit 33, calculates an error between the selection data DS and the target value, and calculates a coupling coefficient W that reduces the error.
i and j are given to the image processing selection unit 33, and by repeating this many times, the image processing selection unit 33 is trained so that the selection data DS approaches the target value.

The image processing section 35 has a number of processing sections 351, 352,..., 35M as image processing means of the present invention. Each of the processing units 351, 352,..., 35M
Performs various processing 1, processing 2,... Processing M with different contents on input image data D1, and outputs image data DO (DV7-0) as a processing result. The processing includes, for example, shading correction processing, LO
G correction processing, density conversion processing, scaling / movement control processing, MT
There are F correction processing, density correction processing (gamma correction processing), and processing combining these. Further, in each of the processes, there may be a plurality of processes in which only the conversion coefficient, the conversion rate, or the correction coefficient is different. The outline of these processes will be described below.

The shading correction process is a process for eliminating unevenness in the amount of light in the main scanning direction. The image data D1 obtained by reading a white plate (not shown) for shading correction is read before shading. The image data D1 obtained by storing the reference data in the memory, converting it into a reciprocal, and reading the original document
And outputs corrected image data D0.

The LOG correction process is a process for converting the electric signal S1 into data proportional to the density since the magnitude of the electric signal S1 is proportional to the reflectance, that is, the luminance of the document. LO
In the G correction process, for example, conversion is performed using a table having a characteristic of density = -LOG (brightness). In addition, since density adjustment according to the operation of the operator is also performed as the density conversion processing, the instruction data DC from the operation unit 116 is used.
Changes the contents of the table according to.

In the scaling / movement control processing, input / output is alternately performed for each line using two line memories for scaling.
By independently controlling the writing timing and the reading timing, zooming and movement in the main scanning direction are performed. In this control, interpolation processing is performed before writing on the reduction side and after reading on the enlargement side, according to the scaling factor, to prevent image loss or image rattling. With this control, an image repeat process, an enlarged continuous shooting process, and a mirror image process are also performed. The magnification in the sub-scanning direction is
This is performed by controlling the moving speed of the mirror 112 and changing the reading speed of the CCD 114.

The MTF correction process is a process for controlling the sharpness of an image and the like. As MTF correction processing,
Smoothing processing or edge enhancement processing is performed. In the density correction processing, density correction (gamma correction) according to the characteristics of the image recording device 12 is performed.

FIG. 3 shows an example of the configuration of the learning section 34, FIG. 4 shows an example of the configuration of the image processing selection section 33, and FIG. 5 conceptually shows an example of the configuration of the image processing section 35. It is. FIG.
, The learning unit 34 includes a target value setting unit 341, an error calculation unit 342, and a coupling coefficient generation unit 343.

The target value setting section 341 sets a target value DV based on the instruction data DC from the operation section 116. The processing units 351 and 3 in the image processing unit 35 are set as the target value DV.
.., 35M are set.
For example, when the selection data DS is the processing units 351, 352,.
If the bit sequence corresponding to 35M is selected and its processing is selected when the corresponding bit is “1” and not selected when the bit is “0”, the selection data DS for selecting the processing unit 351 is It becomes "100 ... 0". The set target value DV is stored in a register in the target value setting unit 341. The selection data DS only needs to be data that can select a processing unit, and the value of each bit is often a decimal value close to “1” or “0” in many cases.

When learning the image processing selecting section 33, the operator sets a document serving as teacher data in the image reading device 11, and selects the optimum processing for the document from the processing sections 351, 352,..., 35M. Instruction data DC so that the target value DV that matches the selection data DS is set. If the operator knows the processing content corresponding to the document serving as the teacher data, the operator sets a target value DV for selecting the processing units 351, 352,..., 35M corresponding to the processing content. If the operator decides the processing content after seeing the actual processing results of the processing units 351, 352,..., 35 M on the document serving as the teacher data, the processing corresponding to the processing content determined in this way , 352,..., 35M
Set.

It has been described that the target value DV is set based on the instruction data DC from the operation unit 116.
Instead of the operation unit 116, a dedicated panel or device for setting the target value DV may be used. In addition, information for setting the target value DV in a document serving as teacher data is recorded in advance without being based on the instruction data DC from the operation unit 116 or an input from a dedicated device,
When the document is read, the information may be read at the same time, and the target value DV may be automatically set based on the information.

The error calculation section 342 includes an image processing selection section 33
Data DS output from the CPU and a target value setting unit 341
And outputs the error ΔD. As a method of calculating the error ΔD, various methods such as a method of averaging the error for each bit, a method of averaging squares, and a method of adding squares are used.

The coupling coefficient generator 343 gives the coupling coefficients Wi, j for each neuron of the neural network of the image processing selector 33. The coupling coefficient Wi, j is 1
Each time, the direction is changed so that the error ΔD becomes smaller.

In FIG. 4, the image processing selecting section 33 is formed by a neural network. The neural network shown in the figure is composed of an input layer S, an intermediate layer A, and an output layer R. The number of neurons in the input layer S is N, the number of neurons in the output layer R is M, and the number of neurons in the intermediate layer A is an appropriate number. Although only one intermediate layer A is shown, there may be two or more intermediate layers A.

Each data of the feature value DH is input to each neuron of the input layer S. A plurality of types of feature values DH may be input. For example, data of a certain feature value DH is input to the first to (N−x) th neurons, and data of another feature value DH is input to the (N−x + 1) th to Nth neurons. Is entered.

Each neuron of the output layer R outputs data corresponding to each bit of the selection data DS. For example, data for selecting the processing 1 from the first neuron, data for selecting the processing 2 from the second neuron, and data for selecting the processing M from the Mth neuron. Data is output respectively. But,
The selection data DS may be decoded by a decoder provided separately, and the processing unit may be selected by this.

In FIG. 5, in the first processing unit 351 of the image processing unit 35, the relation between the input image data D1 and the output image data DO is substantially a quadratic function. The second processing unit 352 receives the input image data D
The relationship between 1 and the output image data DO is almost a logarithmic function. In the M-th processing unit 35M, the relationship between the input image data D1 and the output image data DO is substantially a linear function.

The processing units 351, 352,..., 35M are provided, for example, as tables showing the correspondence between inputs and outputs. The table contains input image data D1.
And an image parameter for outputting the image data D0 of the density value corresponding to the density value of. In addition, it may be provided as a program that generates a function for performing each processing.

Next, a method of using the above-described copying machine 1 will be described. Before using the copying machine 1, the image processing selecting unit 33 is made to learn. In the learning of the image processing selection unit 33, a document serving as teacher data is read by the image reading device 11, and a corresponding target value DV is set from the operation unit 116.
Thereby, the learning unit 34 allows the image processing selecting unit 3
The learning of No. 3 is repeatedly executed. The above-described learning is performed by variously replacing the manuscript as the teacher data. Thus, the optimum coupling coefficient Wi, j is set in the image processing selecting unit 33. The image processing selection unit 33 determines the coupling coefficient Wi, j
Hold. This ends the learning. As the teacher data, for example, a document of a character image, a document of a photographic image, a document of a halftone image, and the like are used.

After that, when the operator sets the original to be copied on the image reading device 11, the characteristic amount DH is extracted from the image data D1 obtained by reading the original, and the image processing selecting unit 33 outputs the characteristic amount DH in accordance with the characteristic amount DH. The selection data DS is output, and an appropriate processing unit is selected from the image processing units 35 according to the selection data DS. Image data D
1 is processed by the selected processing unit and output as image data DO. The image recording device 12 performs printing on the paper PP based on the output image data DO.

According to the copying machine 1 described above, one or a plurality of types of feature values DH extracted from the image data D1 read from the original are input to the input layer S of the image processing selection unit 33, and are output from the output layer R. .., 35M in the image processing unit 35 is selected according to the output selection data DS, so that a copy image of the image quality desired by the user can be easily obtained. In both cases, the circuit scale of the neural network of the image processing selecting unit 33 can be reduced, so that the cost required for the circuit of the neural network and the time required for learning can be reduced.

Further, the target value DV for the teacher data is set, and the image processing selecting unit 33 learns to select the optimum processing units 351, 352,..., 35M. There is no need to set parameters such as thresholds, and optimal image processing can be performed on many general-purpose documents copied by the copying machine 1.

The neural network of the image processing selecting unit 33 only needs to learn a selection method from among a plurality of processing units 351, 352,..., 35M prepared in advance in the image processing unit 35. Abnormal image parameters are not generated unlike the case where image processing is performed. That is, there is no possibility that abnormal image processing is performed.

Next, the edge information and the isolated point information, which are the feature values DH extracted by the feature value extracting unit 32, will be described. First, the edge information will be described. FIG. 6 is a diagram illustrating an example of a Laplacian filter, and FIG. 7 is a diagram illustrating image data edge-emphasized using the Laplacian filter.

The Laplacian filter is often used to increase the contrast at the edge of an image to increase the sharpness of the image (unsharp mask effect). The edge information obtained by using the Laplacian filter and the original image data D1 are added to obtain edge-enhanced image data as shown in FIG.

FIGS. 8A and 8B are diagrams showing examples of the primary differential filter. Using these two types of primary differential filters, image density gradients in the main scanning direction and the sub scanning direction are detected. Edge information is extracted by using the result detected by them.

Next, the isolated point information will be described. In order to detect whether or not the original is expressed in gradation using a dot screen such as halftone printing, the center image (kernel) of each halftone dot that is always present at a fixed period in the halftone original is isolated. An isolated point detection filter, which is a spatial filter for converting the image into an image, is used.

First, using the isolated point detection filter, the central pixel is either higher or lower in density with respect to its peripheral pixels, and the total pixel is higher than the density average in the peripheral pixel 8 direction by a certain level. Extract either larger or smaller. Then, white isolated points and black isolated points in a rectangular area (main scanning: 41 dots / 9 sub-scanning lines) are counted independently, and isolated point information is extracted.

Based on the feature value DH extracted as described above, the image processing selection unit 33 determines, for example, a character image, a photographic image, or a halftone image, and the result is used as the selection data DS. Is output. For example, when the edge information is larger than a certain threshold, the image is determined to be a character image. When the isolated point information is larger than a certain threshold, the image is determined to be a halftone image. Otherwise, it is determined to be a photographic image. Then, according to the type of image (character image, photo image, halftone image, etc.), the selection data D
By S, an appropriate processing unit is selected from the image processing units 35.

For example, in the case of a character image, edge emphasis processing is performed, or processing for increasing the output data with respect to the low-density input data by the processing unit 352 shown in FIG. .

In the case of a photographic image, for example, the processing section 35M shown in FIG. 5 performs a process of making the output data linear with respect to the input data, and maintains continuous gradation.
In the case of a halftone image, a smoothing process is performed using, for example, a smoothing filter. A smoothing filter is a kind of low-pass filter for smoothing an image,
For example, for three pixels in the main scanning direction, 1/4, 1/2,
Integration processing (weighted averaging) is performed at a rate of 1/4, and data obtained by cutting high-frequency components that cause moire is output.

In the above embodiment, the learning unit 34 is provided in the copying machine 1 and the learning unit 34 learns the image processing selecting unit 33 before using the copying machine 1. Such learning may be performed by the manufacturer before shipment from the factory. Also in this case, the user of the copying machine 1 may perform the processing in a timely manner according to the preference of the operator or the specialty of the document.

The learning unit 34 is not provided inside the copying machine 1, but is provided outside the copying machine 1. The copying machine 1 is provided with a connector and an interface for connection with the external learning unit 34. It may be. In that case,
The external learning unit 34 can be configured as a dedicated learning unit, or can be configured by loading a learning program into a general-purpose personal computer. In the case of such a configuration, the learning unit 34 is connected only when the user of the copying machine 1 or the service person learns the image processing selecting unit 33, and the normal use as the copying machine 1 is performed. Can be obtained by removing the learning unit 34.

When performing learning, the image processing unit 35
, 35M prepared in advance, only the processing unit actually used is selected and designated, and the number of types of image processing can be set as the target number. In that case, the coupling coefficient of the output layer R corresponding to the unused processing unit may be fixed to “0”.
Therefore, the learning time of the neural network can be further reduced by minimizing the target number according to the actual use of the copying machine 1. In addition, by reducing the target number, it is possible to easily set the target value DV by operating the operation unit 116.

Further, since the total number of neurons constituting the neural network can be determined by setting the target number, the target number corresponding to the set target number is determined based on the experimental values of the learning time obtained for various numbers of neurons. A predicted value of the learning time can be obtained. Therefore, by displaying the estimated value of the learning time obtained on the screen,
It is helpful for the user to determine the target number, and the target number is easy to determine.

In the above-described embodiment, the image processing is performed on all the originals set on the image reading apparatus 11 by all the processing units 351, 352. And the operator may select an appropriate process from among them to set the target value DV.

Then, the processing units 351, 352,..., 35 M are selected by looking at the result of the actual image processing, so that the operator does not need to consider the contents and characteristics of the processing, and the setting of the target value DV is easy. It is not wrong. Therefore, it is not necessary to have specialized knowledge on image processing or image characteristics, and even a user without specialized knowledge can easily set the target value DV.

In this case, the result of the image processing may be displayed on the screen instead of being printed on the paper PP. Each image may be printed or displayed, or a plurality of images may be listed and printed or displayed.

In the above embodiment, the image processing unit 3
, 35M are prepared in advance, but a processing unit capable of rewriting the processing contents by a user may be provided so that customized image processing can be set. Further, some processing units in the image processing unit 35 are provided outside the image processing unit 35, and the image data D
1 may be always or selectively executed.

In the above embodiment, a three-layer neural network is used as the neural network of the image processing selecting unit 33, but a four-layer or more neural network may be used. The number of neurons in each layer,
The presence or absence of the coupling coefficient, the type of the response function, the learning method, and the like can be variously changed in addition to the above. The neural network can be realized by a computer simulator. That is, for example, the learning unit 34
, Or a table representing the learned coupling coefficients and response functions, and a program for calculating and retrieving them. Also,
It can also be realized by hardware.

In addition, the image processing selecting section 33, the learning section 3
4. The configuration of the image processing unit 35 or the image processing unit 115, the processing content, the processing sequence, the processing timing, the configuration of the copying machine 1, and the like can be appropriately changed in accordance with the gist of the present invention.

In the above-described copier 1, the image processing or image correction in monochrome has been described as an example, but it goes without saying that the present invention can be applied to color image processing or image correction.

[0068]

According to the first to fourth aspects of the present invention,
Using a relatively small-scale neural network, the time required for learning can be reduced, and no abnormal image processing means is generated for a general-purpose document. A desired image quality can be easily obtained by selecting an appropriate one from among them.

According to the third aspect of the present invention, it is possible for the user to learn so that appropriate image processing is performed according to the original or the like used.

[Brief description of the drawings]

FIG. 1 is a diagram showing a schematic configuration of a digital copying machine provided with an image processing unit according to the present invention.

FIG. 2 is a block diagram illustrating an overall configuration of an image processing unit.

FIG. 3 is a diagram illustrating an example of a configuration of a learning unit.

FIG. 4 is a diagram illustrating an example of a configuration of an image processing selection unit.

FIG. 5 is a diagram conceptually illustrating an example of a configuration of an image processing unit.

FIG. 6 is a diagram illustrating an example of a Laplacian filter.

FIG. 7 is a diagram showing image data edge-emphasized using a Laplacian filter.

FIG. 8 is a diagram illustrating an example of a primary differential filter.

[Explanation of symbols]

32 feature amount extraction unit (feature amount extraction unit) 33 image processing selection unit (image processing selection unit, neural network) 34 learning unit (learning unit) 35 image processing unit 115 image processing unit (image processing device) 351, 352, 35M Processing unit (image processing means)

Claims (4)

[Claims] An image processing apparatus which performs image processing of contents according to characteristics of the image data and outputs the input image data, wherein a plurality of types of image processing means having different contents of the image processing are provided. Image processing unit, a feature amount extracting unit that extracts a feature amount of the input image data, and a neural network that selects an appropriate image processing unit corresponding to the feature amount from the plurality of types of image processing units An image processing apparatus, comprising: 2. An image processing apparatus for performing image processing of contents according to the characteristics of the input image data and outputting the processed image data, wherein a plurality of types of image processing means having different image processing contents are provided. An image processing unit, a feature amount extracting unit for extracting a feature amount of the input image data, and selection data for inputting a feature amount to an input layer and selecting an image processing unit corresponding to the input feature amount. A neural network trained to output from an output layer, and an appropriate image processing unit corresponding to the feature extracted by the feature extracting unit is selected from the plurality of types of image processing by the neural network. An image processing apparatus, comprising: 3. An image processing apparatus for performing input image processing on image data having a content according to the characteristics of the image data and outputting the processed image data, wherein a plurality of types of image processing means having different image processing contents are provided. An image processing unit having: a feature amount extraction unit that extracts a feature amount of the input image data; a feature amount extracted by the feature amount extraction unit is input to an input layer; A neural network that outputs selection data for selecting one image processing unit from the output layer from an output layer, and selection data that inputs a feature amount to the input layer and selects an appropriate image processing unit corresponding to the input feature amount And a learning means for learning the neural network so as to output the neural network from the output layer. 4. The image processing apparatus according to claim 1, wherein said plurality of types of image processing means include image parameters for outputting image data having a density value corresponding to a density value of input image data. An image processing device according to any one of the above.