1. Field of the Invention
The present invention relates to a .gamma.-correction (compensation) curve selecting apparatus and a .gamma.-correction (compensation) curve creating apparatus, and in particular, a .gamma.-correction (compensation) curve selecting and creating apparatus capable of being preferably utilized in image processing systems such as digital black-and-white copying machine, color facsimile device, scanner, printer, and so
The technical term ".gamma.-correction" refers to gamma correction or compensation. Hereinafter, the word ".gamma.-correction" will be used.
2. Description of the Prior Art
In a conventional black-and-white copying machine an image processing treatment is performed by use of the image processing system as shown in FIG. 40. Namely, after a manuscript document such as a book or a copied sheet is set on the manuscript document stand and the document is read out by a CCD sensor, the image signal emitted from the CCD sensor is converted from an analog signal to a digital signal by use of an A-D converter, and thereafter, shading correction, .gamma.-correction, density conversion, correction of output .gamma.-correction, binarizing or multi-valuing process treatment are performed in order. In this manner, an image recorded using toner or other material by the use of a printer or the like is created. This is the prior-art method of image processing. Furthermore, it is also known to perform dither image processing after correcting an output .gamma.-correction as shown in FIG. 42.
In such a single-color image processing apparatus in a digital black-and-white copying machine, the single-color printer or the like, there are the following problems.
(1) The .gamma.-characteristics of respective image scanners differ from each other, and the .gamma.-characteristics are not completely uniform. (See, for example, "Color Image Scanner Designing Technology--Trikepps, WHITE SERIES, No. 130 pp. 123-126). PA1 (2) The output .gamma.-characteristic is not uniform due to the relationship of the pitch of writing-out and the dot system. (See also, "Color Image Scanner Designing Technology--Trikepps, WHITE SERIES, No. 130 pp. 157-158). PA1 (3) As to the .gamma.-correction, the value of .gamma.-correction is given to the above single-color image processing apparatus from an external source. See Trikepps, WHITE SERIES, No. 130 pp. 123-126, supra. PA1 (1) shading correction for correcting the unevenness of illuminating the manuscript document and the unevenness of the sensitivity; PA1 (2) .gamma.-correction for correcting the .gamma.-characteristic of the sensor; PA1 (3) density conversion for converting the light intensity received by the sensor to a density value; PA1 (4) MTF correction for correcting the spreading response for the point input; PA1 (5) noise removal; PA1 (6) color correction; and PA1 (7) enlargement and reduction processings at the respective halftone levels. PA1 y: Output Voltage PA1 x: Input Light Intensity PA1 a: Sensitivity of the Sensor PA1 b: Output Voltage at Darkness PA1 .gamma.: Correction Coefficient PA1 D: Density PA1 Io: Intensity of Incident Light Rays PA1 I: Intensity of Reflected Light Rays PA1 1) Instead of directly performing the logarithmic operation, density conversion is realized with the LUT system constructed with the ROM, as can be seen from FIG. 52. PA1 2) An analog logarithmic amplifier Is added to the A-D converter at the previous stage. PA1 3) Logarithmic conversion and A-D conversion are realized at the same time using the logarithmic A-D converter. PA1 (1) The characteristics of color dissolving filter, and the transmission factor and spectrum reflection factor are not ideal. (Refer to "Basic and Application of Electrophotographic Technology", pp. 565-576.) PA1 (2) Duplicating of toner is not ideal. (Refer to "Basic and Application of Electrophotographic Technology", pp. 565-576.) PA1 (1) low density and poor depth; PA1 (2) hardness (lacking in softness); and PA1 (3) frequent unevenness. PA1 (1a) a problem of the color reproductivity; PA1 (2a) another problem of the halftone reproductivity; and PA1 (3a) still another problem inherent to the electrophotographic apparatus. PA1 1) repeatability or consistency of the color reproductivity, and PA1 2) faithful reproductivity of the color (color matching to the original manuscript document). PA1 8-1) Linear Masking Method: PA1 8-2) Non-linear Masking Method: PA1 8-3) (Black) Inking, UCR:
Regarding the above-noted drawbacks (1)-(3), further explanation will be given hereinafter. First, the outline of the image processing and .gamma.-correction will be described. The image scanner has been fixedly employed as an apparatus for taking in the image data in the field of the electronic publishing (EP) or the desk-top publishing (DTP). Taking-in of the photograph or the printed document as the image data into the personal computer (PC) enables not only replacement of the cut-off photographs by the other image group with electronic processing also various other image processing methods such as the emphasis or color conversion for the taken-in image data.
If the two image data can be composed or various design constructions realized on the display, in practice, without employing any coloring materials, it may be possible to obtain higher efficiency or to draw upon the new creativity of the designer. Furthermore, the data-basing of the image or the image communication may cause the world of image to approach to us. In a broader sense, character recognition may also be thought of a sort of image processing. Therefore, many applications exist utilizing various types of image processing in the field of image technology. On the other hand, various image processing are also executed in an image scanner used for taking in such image data into the PC. Fundamentally, those types of image processing are thought to be a processing of absorbing the characteristic of the reading-out system. The correction for the difference of the sensor and/or the unevenness of the light intensity of the optical system is basically different from the image processing for the reading-out of image data.
Such correction may be termed "previous processing" for inputting the read-out standard image data into the PC. Usually, this previous processing will include shading correction, density conversion, .gamma.-correction, MTF correction, etc., which are explained later. The fundamental image data are converted to data of the desired size by correcting the characteristics of the reading-out system. Or otherwise, the halftone data read out with a desired resolution are captured and thereby the enlargement and reduction processings are executed with some halftone levels as previous processing.
On the other hand, subsequent processing is an applicable processing based on the image data with the standard halftone. Although the binary processing are usually executed on the side having the image scanner, it is unavoidable from the viewpoint of the present situation of high-leveling to the extent that the transferring amount of the data from the image scanner to the PC or the binary processing itself is needed to employ the specially-used hardware. Further, binary processing frequently has no relation to the concretely-executed processing in a system concerned with binary images.
The binary processing is a type of intermediate processing between the previous processing and the subsequent processing. However, in order to execute binary processing, .gamma.-correction at the output side or the color correction (compensation) has to be performed prior to the binary processing. The image scanner integral with the system as opposed to being a typical input device for the use with standard image data. As to the subsequent processings, in addition to the above, there are output .gamma.-correction, moire removal, and compression coding of the image which is necessary for exchanging image communication or creating the image data base.
The previous processing technique will be explained. Previous processing of the image processing in the image scanner is defined as a series of processings from the operation of reading out the original manuscript document and correcting the characteristic of the reading-out system to the operation of obtaining the data with the standard halftone.
The above previous processing includes the following functions:
These functions must be executed before various image processing methods can be performed correctly. The functions are to be performed apparatus inside the scanner.
The enlargement/reduction processings at the respective halftone data levels, namely, the processings in which the halftone data are input and the same are output in order to output the halftone data of optional size from the scanner is a fundamental function of the scanner. Therefore, the enlargement/reduction processings should be included in the previous processing. Although the binary processing is the fundamental function for the system treating the binary image, the same is not a fundamental function for other systems concerned with halftone images. For this reason, the latter function is part of the subsequent processing.
The respective functions are described, hereinafter. First, the shading correction will be described. The system of reading out the image can monotonously reproduce the density value of the image as an ideal function. However, in practice, even though the manuscript document of uniform density is read out, the output signal causes unevenness depending on the pixel, for the reason of non-uniform intensity (brightness) of the light rays radiating the document surface and the non-uniform characteristics of the sensor. Shading correction is a method of performing correction so as to obtain a form output for the manuscript of uniform density.
FIG. 49 shows the photoelectric conversion characteristic of the CCD image sensor. In FIG. 49, the relationship between the input light amount (intensity) [Ix. sec] and the output voltage [v] is generally expressed by the following equality: EQU y=ax.sup..gamma. +b (1)
When a CCD is employed, the correction coefficient .gamma. is approximately equal to 1. In the case of reading out the image by use of a line sensor, unevenness in the sub-scanning direction can be ignored and the shading correction is performed for each line of the main-scanning direction on the basis of white and black standard data. Further, regarding the correction of the black standard data, almost all of the correction depends on the dark current and is largely influenced by the ambient temperature. However, since the unevenness is comparatively small between the respective pixels, the data are treated as being constant during the time period of reading out one sheet of manuscript document. When high precision is demanded for the apparatus, it is necessary to perform the correction for both black standard data and the standard data.
Next, the method of the digital operation (first shading correction method) is described referring to FIG. 50. First, black standard data are created by reading out the black standard original document or outputting the data in a state where the light source is turned off, and the created data are stored in the correction memory A. Next, the white standard original document is read out and the data created by subtracting the previously stored black standard data from the white standard data are stored in the correction memory B. When the original document is read out, the value of the correction memory A is subtracted from the data of the original document. Next, the subtracted value is divided by the value of the correction memory B. In this manner, shading correction is performed.
Next, the method of controlling the reference voltage of the A-D converter (second shading correction method) is described referring to FIG. 51. At first, the reference voltages +REF and -REF of the A-D converter are set, respectively, as follows: EQU +REF=E;
and EQU -REF=0,
and the black standard data and the white standard data are, respectively, read out. The respective read-out outputs(data) are stored in the memories A and B respectively. When the original document is read out, the operation of the shading correction can be done using the reference voltages +REF and -REF which are respectively created by reading out and D-A converting the data in the correction memories A and B.
Now, .gamma.-correction will be described. In the equality (1), when the y-value is not equal to 1, the relationship between input and output becomes non-uniform, the operation of correcting (compensating) the relationship therebetween so that it becomes linear is called .gamma.-correction. The .gamma.-characteristic of the sensor may be approximately 1.0 (photodiode) or 0.6 to 1.0 (photo-electric element). It can be simply applicable to concretely realize the .gamma.-correction by use of the LUT (Look Up Table) constructed with the ROM (Read Only Memory). For example, the treatment of the input/output relationship is performed with one-time LUT-treatment and density conversion mentioned below, and thereby the deterioration of the precision can be suppressed to a minimum.
Moreover, .gamma.-correction is performed for the purpose of correcting the characteristic of the output apparatus or positively changing the .gamma.-characteristic of the image. Such methods for performing the .gamma.-correction are described later.
Now, density conversion will be described. The optical sensor responds to the physical amount represented by the unit of the light intensity [Ix-sec.] On the other hand, when the light intensity becomes the visual excitement, the density which is physical amount sensed visually can be expressed by the logarithm of the light intensity as shown by the following equality: EQU D=log (1/T) or log (Io/I) (2)
The above equality (2) is well-known as the rule of "Weber-Fechner". When the image data read out by the sensor are output from the scanner, the data should be the density value usually. Therefore, it is desirable to convert the light intensity to a density value before performing the various image processing steps at the previous (preceding) stage, for the purpose of suppressing the maximum amount of information. For this reason, density conversion is performed using the above equation (4) after the operation of shading correction. To state this more concretely, there are the following three methods of density conversion.
Usually, method 1) is utilized because it is beneficial from the viewpoint of cost and can be realized at the same time when the data are converted by use of the other LUT such as .gamma.-correction. However, in the case of utilizing the LUT system, it is necessary to always pay strict attention to any deterioration in precision. It is desirable to have approximately a two bit margin for the precision of the input data when the conversion and the density conversion are performed.
The output .gamma.-correction will be further explained below. The dot form of the output apparatus for actually outputting the image processed binarily differs from each other according to the output system. In the ink-jet system or the electrophotographic system, round (circular) dots are usually employed.
As shown in FIG. 53, it is necessary to make the dot diameter slightly larger than the writing-in pitch such that, when all of the dots strike the recording medium, a wide black-area can be realized without causing any white gaps. Consequently, the dot number and the printed area are not precisely proportional to each other. Further, the relationship differs depending on whether the sort of binarizing is a dot-dispersing type or a dot-concentrating type. Therefore, in order to obtain the linear density-output characteristic, it is necessary to perform .gamma.-correction in consideration of the actual size of the dot and the extent of overlapping therebetween.
In practice, the .gamma.-correction curve can be determined by actually measuring the density value of the output image at the gray level. Namely, assuming that the output characteristic curve which shows the relationship between the output halftone data and the hard copy density and is as expressed in the fourth quadrant of FIG. 54, the curve symmetrical to the above curve regarding the y-axis can be used as the .gamma.-correction curve. It is possible to see FIG. 54, following the order of the original document density, scanner output value, output halftone data, and hard copy density, starting at the original document density. The relationship between the original document density and the scanner output value becomes a straight line of with 45.degree. inclination as shown in the second quadrant in total consideration of the light intensity--density conversion and the f-correction of the sensor. The hard copy density obtained through the .gamma.-correction curve and the output characteristic curve can be obtained from a linear relationship of 45.degree. inclination with the original document density because of the symmetrical relationship of those two curves. The realization of the .gamma.-correction can be performed by use of the LUT.
In addition to the .gamma.-correction for obtaining the density linearity, the halftone characteristic Is positively converted, on some occasions, for the purpose of converting a dark original document to a brighter one or emphasizing the medium density portion. FIG. 55 shows the various sorts of the halftone converting curve. Only the large-inclination portion is emphasized.
Finally, various corrections will be explained in connection with the actual products. The .gamma.-correction in the image processing apparatus such as the digital black-and-white copying machine, the single-color printer, or the like can be realized by use of a Look Up Table (called "LUT", hereinafter) 91 as shown in FIG. 2. In case that .gamma.-correction of an image inputting apparatus such as a scanner, etc., is performed in the digital black-and-white copying machine, the input signal of the image processing apparatus is a reading-out signal which is the output signal from the image inputting apparatus, and the output signal thereof is the value of the .gamma.-correction for the input signal. On the other hand, in the case of performing the .gamma.-correction of the printer portion in the digital black-and-white
986. copying machine or that of the image outputting apparatus in the printer or the like, the input signal of the image processing apparatus is a writing-in signal transmitted to the image outputting apparatus in the printer or the like, and the output signal thereof is the .gamma.-correction value of the input signal. FIG. 9 shows an example of the relationship between the input signal and the output signal .gamma.-characteristic curve). LUT91 is constructed with a ROM accommodating a plurality of .gamma.-characteristic curves. As shown in FIG. 2, one of .gamma.-correction curves is selected in accordance with a table selection signal and the output signal is determined by the selected .gamma.-correction curve. The .gamma.-characteristic of the image inputting apparatus or that of the image outputting apparatus determines r correction. For instance, the .gamma.-characteristic of the scanner determines the .gamma.-correction as shown in FIG. 4 or the .gamma.-characteristic of the single-color printer determines the same characteristic as shown in FIG. 6.
Further, in the digital color copying machine, image processing is performed by use of the image processing system as shown in FIG. 41. Namely, the manuscript document is set on the document stand and the image on the manuscript document is dissolved into three colors by the color dissolving filter and the dissolved colors are read out by the CCD sensor. The read-out data are treated with A-D conversion, shading correction, .gamma.-correction & density conversion, color conversion & color correction, groundwork (background) removal (called "UCR", hereinafter), output .gamma.-correction, binarizing or multi-valuing. The treated data are the ones to be employed as the value of writing-in by the printer with four toner-colors Y (Yellow), M (Magenta), C (Cyan), and K (Black). After the above treatment, the data are transferred to the printer.
The present invention closely relates to image processing by use of a neural network system; that is, the .gamma.-correction curve selection and creation. The study of applying a neural network to the pattern recognition, the signal processing, and the intelligence treatment has been made progress in recent years, because of the success of the studying algorithm using the neural network "Backpropagation" announced in 1986. The neural network system can be studied automatically by offering the input and the desirable output. Underseas, rock and submarines can be discriminated from each other by use of the sound emitted from the sonar and reflected off the object such as the above-mentioned rock or submarine, by use of the neural network system. The English text is converted to the phonetic symbols (signs) in the system. In such application as mentioned above, a result equal to or better than that of the conventional system can be obtained in the aforementioned neural network system. There exists an intermediate layer between the input layer and the output layer in the employed neural network system. During the time period of continuing the study, the circuit necessary for recognizing the pattern is formed in the intermediate layer.
In this situation, there has been introduced a new capable of giving instructions such as a pattern recognizing functions or algorithm knowledge processing functions to the neural network system.
In comparison to the ability of the operation (for instance, three operators) trained for about two years with that of the neural network system, the recognition factor of the human is 88%-93%, while that of the neural network system turns out to be 99% (for the studied data) or 92% (for the data which has not been studied). Regarding the characteristic extracting system utilizing the AI (Artificial Intelligence) Technology, the recognition factor of the system turns out to be 91.5% (for the studied data) or 84% (for the data which has not been studied).
For the neural network, the studying algorithm is a key. The neural network itself is a network imitating algorithm the human brain. The individual construction elements of the neural network system have functions similar to human nerve cells (neurons). On the other hand, the structure and the function thereof are not uniform. The neural network system can be roughly divided into two types as will be described later. The algorithm for studying the system is called "backpropagation". The feature of the algorithm is that even the non-professional can study the neural network. It is not necessary for the professional to study the neural network. It is sufficient to have only the knowledge of the problem to be applied. The studying of the algorithm of the neural network is advanced as follows. First, the input is applied to the neural network system, and then the output of the system is reached. If the output thereof is incorrect, the correct output is taught to the neural network system. Then, the system changes its internal structure (the strength of the connection of the network) so as to emit the correct output. After repeating the aforementioned operation, the studying of the neural network is complicated. The range of the neural network systems application is very wide. The system is applied to pattern recognition, voice recognition and composition, signal processing, knowledge treatment, etc. There are many remaining problems not solved in the conventional system. The study of solving these problems by use of the neural network system and its studying algorithm have been vividly performed in recent years.
The structure of the neural network system, the method of studying the neural network, and the results of several applications thereof are introduced hereinafter. The neural network constructed in a classified structure can be studied by use of backpropagation. The neural network is a network imitating the human brain, as mentioned above. A plurality of units corresponding to the neurons of the human brain are complicatedly connected to each other. It is possible to embed the pattern recognizing function and the knowledge processing function in the units of the neural network by suitably deciding the operation of the respective units and the connecting state between the respective units. The structure of the neural network unit is relatively simple. It is a model of a neuron. The simple structure consists of an input portion for receiving the input from the other unity an intermediate portion for converting the received input to the other under a constant rule, and an output portion for outputting the result. The connecting portion of the respective units for connecting to the other unit has, respectively, a variable weighing "wij" attached thereto, in order to express the strength of connection. If the value of this strength is changed, the structure of the neural network is also changed. The value "w" takes positive, zero, and negative values. A zero value signifies no connection therebetween.
The neural network adopts the classified structure as shown in FIG. 56. There exists an intermediate layer between the input layer and the output layer. The intermediate layer is called a "hidden layer". The neural network of the other structure exists also as described later. Here, the explanation thereof is limited to the neural network of the classified structure in which the backpropagation algorithm can be utilized.
The network takes a structure in which the input layer, the intermediate layer, and the output layer are connected to each other in the direction of the above order. There is neither connection in the respective (same) layers, nor connection in the direction from the output layer to the input layer. The network is a feedforward type, that is, the network advancing only forward. It takes a structure similar to the "perception". The ability of the network has been enhanced prominently, by arranging the intermediate layer therebetween and making nonlinear the input/output characteristics of each unit.
The studying of the network is executed as follows. First, the input data is applied to the respective units of the input layer. Next, the input signals (data) are converted to their respective units, and the converted signals are transmitted to the intermediate layer. Finally, the signals are discharged from the output layer. The output values are composed with a desirable value by use of a comparator. Finally, the strength of the connection is changed so as to reduce (or eliminate) the difference therebetween. When the intermediate layer exists, the studying becomes difficult because it is impossible to know which strength of connection is the reason for the occurring error. Backpropagation is the studying algorithm of such multi-layers network proposed by Rumelhart, of the California University, San Diego, in 1986. The neural network can be classified into two types by the structure thereof, a pattern associator type and an automatic associator type, as shown in FIGS. 57a and 57b. A representative of the former type is the network proposed by Rumelhart, Hinton, et al., while that of the latter type is the network proposed by Hopfield. The pattern associator type is the network capable of converting the input pattern to the (other) output pattern. For instance, an XOR (exclusive OR) operational unit can be constructed with such a network.
On the other hand, the automatic associator type is the network in which a plurality of patterns are stored therein, and a pattern nearest to the input pattern is outputted therefrom. For instance, the faces of a hundred persons are stored as the images in the network, and when the face of a person including noise is input to the network, the network can correctly reproduce the input face. The network functions as an associator memory. The above-mentioned two types of network differ from each other in the construction and the method of storing the pattern. In the pattern associator type, each network is constructed with the respective units (neurons) in the input layer, the intermediate layer, and the output layer. Each unit is connected to the other in a direction from the input layer to the output layer. The units in the same layer (one of the respective layers) are not connected to each other. The input unit (layer) and the output unit (layer) are independent. In the automatic associator type, the input unit and the output unit are common. All units in the network are connected to each other. The operational function and the image information are stored in the memory as the connection status between the respective units and the strength of the connection. In the pattern associator type, the connection strength is changed by use of the difference between the obtained output and the desirable output.
On the other hand, in the automatic associator type, the connection strength is changed so as to discriminate the similar input patterns. The studying algorithm for both types have been already proposed. The aforementioned backpropagation is the studying algorithm of the pattern associator type.
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