Image processing apparatus utilizing a neural network to improve printed image quality

An information processing apparatus including a switch for manually requesting change of output image quality, detecting unit for detecting a condition of the apparatus, setting unit of setting an image forming condition in accordance with the detected condition and the requested output image quality and control unit for changing the image forming condition by learning the request previously requested by the user. Image forming conditions are adjusted so as to satisfy the user manually based upon the degree of satisfaction determined by the user.

BACKGROUND OF THE INVENTION
 1. Field of the Invention
 This invention relates to an information processing apparatus in which
 picture quality is capable of being adjusted at user's requirements.
 2. Prior Art
 In information processing apparatus such as copying machines and printers,
 generally a variety of measures are taken in order to maintain the quality
 of an outputted image at a fixed level. As one example, an apparatus of
 the aforementioned kind may be provided with control means for directly
 controlling picture quality, wherein control parameters are set in the
 control means in appropriate fashion. In addition, the apparatus may be
 equipped with various sensors in order to optimize the control parameters
 in conformity with changes in the environment in which the apparatus is
 used, wherein it is possible to perform highly sophisticated control of
 the type in which the control parameters are automatically adjusted to
 appropriate values frequently in dependence upon the outputs from the
 sensors. For example, in the case of an electrophotographic copying
 apparatus, control means for so-called development bias control is
 provided in order to make the copy density, which is an important factor
 in picture quality, conform to a fixed standard. The control means is so
 adapted as to set the bias value to a value which is appropriate.
 It has recently become possible to perform development bias control more
 finely. Specifically, accordingly to such control, ambient temperature,
 ambient humidity, the density of an original document, etc., are sensed by
 sensors and the appropriate bias values are calculated based upon the
 sensor outputs in order to stabilize picture quality.
 However, in the examples of the prior art described above, ordinarily the
 values of the control parameters are fixed values decided at the design
 stage and these cannot be changed following shipment of the apparatus from
 the factory. Furthermore, in a case where changes in environmental
 conditions are sensed by a plurality of sensors and the values of the
 appropriate control parameters are revised, the mapping relationship
 between the sensor output values and the appropriate control parameter
 values is decided at the design stage and this relationship does not
 change once it has been decided.
 Accordingly, in order to achieve uniformity of quality in terms of picture
 quality and density, etc., as the final objective using the
 picture-quality control methods of the kind set forth above, a mean having
 some latitude is taken of the demands made by all users and the mean must
 be adopted as being representative of the appropriate value. Consequently,
 since the appropriate values are merely the representative values set at
 the time of shipment, there will always be some users who are dissatisfied
 with the picture quality obtained. A similar problem arises even in
 control for automatic adjustment of copy density for dealing with the
 density of the original document. Such control is employed widely in the
 latest copying machines. Specifically, the problem is that when the
 automatic adjustment function is implemented, copies always appear
 somewhat faint. Consequently, even though the automatic adjustment
 function is intended to be convenient, there are only a few users who make
 the adjustment manually rather than utilize the automatic adjustment
 function.
 Further, in the case of a copying apparatus of the electrophotographic
 type, the parameters which affect picture quality are highly numerous and
 a variation in picture quality owing to the passage of time and slight
 differences in the characteristics of various components are unavoidable.
 Thus, the conventional image forming apparatus still possess a variety of
 shortcomings.
 SUMMARY OF THE INVENTION
 Accordingly, an object of the present invention is to provide an image
 processing apparatus in which it is possible to set image forming
 conditions conforming to the state of the apparatus at use and in line
 with desired requirements.
 According to the first aspect of the present invention, there is an
 information processing apparatus provided which comprises:
 means for manually requesting change of output image quality;
 detecting means for detecting a condition of the apparatus;
 setting means for setting an image forming condition in accordance with the
 detected condition and the requested output image quality; and
 control means for changing the image forming condition by learning the
 request previously requested from the requesting means.
 According to the second aspect of the present invention, there is provided
 an information processing apparatus which comprises:
 means for requesting change of output image quality;
 detecting means for detecting environmental parameter;
 determining means for determining an image forming condition by computing
 detected environment-parameter;
 learning means for memorizing the determined image forming condition by the
 determining means; and
 image recording initiating means for carrying out subsequent image
 recording utilizing the value learned.
 According to the third aspect of the present invention, there is provided
 an information processing apparatus having image forming condition setting
 means for setting the desired image forming conditions and image forming
 means for forming the image under the set image forming conditions
 comprising:
 satisfaction discriminating means for determining whether or not a user is
 satisfied with an output image by monitoring operation of the apparatus
 performed by the user;
 environmental-parameter detecting means for detecting
 environmental-parameters;
 control means for deciding the amount of adjustment based upon detection
 values outputted by said detecting means and;
 a neural network provided in said image forming condition determining means
 for obtaining at least one image forming condition by performing
 computation of the environmental-parameter input adjusted by the control
 means;
 whereby image forming conditions are adjusted so as to satisfy the user
 based upon the degree of satisfaction determined by said satisfaction
 discriminating means.
 According to the fourth aspect of the present invention, there is provided
 an information processing apparatus comprising:
 a neural network for processing an input information thereto on the basis
 of a predetermined connectivity constant and outputting processed
 information;
 a control means for setting a connectivity constant to said neural network;
 a memory means for storing a program for computing the connectivity
 constant to be set by said control means; and
 wherein said neural network, said control means and said memory means are
 arranged on a single substrate.
 Other features and advantages of the present invention will be apparent
 from the following description taken in conjunction with the accompanying
 drawings, in which like reference characters designate the same or similar
 parts throughout the figures thereof.

DESCRIPTION OF THE PREFERRED EMBODIMENTS
 Embodiments of the present invention will now be described in detail with
 reference to the accompanying drawings.
 (First Embodiment)
 FIG. 1 is a block diagram illustrating a copying apparatus of a first
 embodiment in which the present invention is applied to a copying machine
 of the electrophotographic type.
 As shown in FIG. 1, the apparatus includes a glass platen 1, an imaging
 lens 2, a photosensitive drum 3, a light source 4, a reflecting mirror 5,
 a developing drum 6, a primary corona charging device 7, a transfer
 charging device 8, a charge removing needle 9, an optical fiber 12 for
 detecting the amount of light from the light source 4, an optical fiber 13
 for detecting the density of an original, and a controller 14 for
 controlling the overall system of the apparatus, which provides a
 microcomputer (CPU) 51, ROM 53 for storing therein a system control
 program, and RAM 54, which is used for the working area. Numeral 10
 denotes toner, and numeral 11 represents toner waste.
 The apparatus further includes a high-voltage power supply 15 for
 performing primary corona charging, development biasing, image transfer
 and charge removal, a control panel 16 having keys which the user operates
 to adjust copy density, as well as disable presentation keys, a
 temperature sensor 17 for sensing ambient temperature T, a humidity sensor
 18 for sensing ambient humidity, a main operation signal line 20 of the
 high-voltage power supply 15, an ON-OFF operation signal line 21 for the
 AC portion of the development bias, and a control signal line 22 for
 controlling the value of the DC portion of development bias. The apparatus
 is provided with a neural network consisting of an input layer 25, a
 hidden layer 26, an output layer 27, a section 28 for the input
 connections, and a section 29 for the output connections.
 Numeral 30 denotes a bus which sets weighting constants for the input and
 output connections 28 and 29.
 In this embodiment, five items are set as various environmental parameters
 related to picture quality. These are original density N, ambient
 temperature T, ambient humidity M, total number C of copies, and number D
 of copies to be made in a fixed period of time. As means which the user
 may employ to adjust copied results to a desired picture quality, mention
 can be made of a density adjustment button. Such a button has two sides,
 one for increasing density and one for decreasing density. When the user
 does not press the button to either side, an amount of adjustment
 initially set by the controller is internally assigned and the control
 panel indicates neutral.
 In the case of this embodiment, the adjustment of density is carried out by
 controlling the potential (the developing DC bias) of the developing drum.
 The amount of adjustment is decided by the neural network 25.about.29
 based upon the environmental parameters.
 Operation up to optimization of density will be described hereinafter with
 referring to a flow chart illustrated in FIG. 2. The control sequences
 performed by FIG. 2 is stored in memory ROM 53 of the controller 14.
 First, an adjustment amount V for values of f(N,T,M,C,D), which were
 determined by the previous copying operation, is outputted at step SI in
 the flowchart of FIG. 2 in accordance with a mapping relationship between
 environmental parameters and amount of adjustment decided previously, and
 the copying operation is started at step S2 of the flowchart. If the user
 is satisfied with the results of copying upon observing the results (YES
 at step S3), then the user continues copying or terminates the copying
 operation without using the adjustment button. On the other hand, if the
 user is not satisfied (NO at step S3) and adjusts density to the desired
 density by using the adjustment button, V' is substituted for the value of
 the adjustment amount at step S6 in FIG. 2. Next, at step S4, the set
 (N,T,M,C,D,V') of environmental parameters and amount of adjustment
 prevailing when it is judged that the user is satisfied is stored in
 memory (not shown), which is provided within the controller 14, as a first
 teaching pattern. Then, at step S5, a new weighting is reset for the input
 and output connections 28 and 29 of the neural network.
 Next, on the basis of the teaching pattern registered previously, and the
 teaching pattern currently registered, the connectivity constants of the
 neural network 25-29 are optimized at step S5. In other words, the
 function f is updated.
 The program then returns from step S5 to step S1, where copying starts
 being carried out at the new .nu. so that it may be judged whether the
 revised density is satisfactory. When the desired density is reached,
 copying is continued as is or the copying operation is terminated.
 Accordingly, the amount of adjustment which the user finds satisfactory
 can be estimated through such a series of operations. When a teaching
 pattern is stored, in actuality a restriction is imposed by memory
 capacity. In any case, it would be necessary to introduce an algorithm for
 accumulating a fixed quantity of the latest patterns or for updating them
 in relation to those which satisfy certain conditions. There is no
 limitation as to which algorithm is used.
 There are several well known algorithms for optimizing the connectivity
 constants of a neural network, one of which is the back-propagation
 method. There is no limitation as to which algorithm is used.
 The general features of an electrophotographic process will now be
 described.
 First, an original placed upon the glass platen 1 is irradiated with light
 emitted by the light source 4, and the reflected light is imaged upon the
 photosensitive drum 3 via the imaging lens 2. The photosensitive drum 3 is
 charged in advance by the primary corona charging device 7 and is
 sensitized in accordance with the imaged pattern so that a latent image of
 the electrostatic pattern is formed. The latent image is developed and
 rendered visible in toner by the developing drum 6. Next, the toner image
 is transferred from the photosensitive drum 3 to copy paper (not shown) by
 the transfer corona charging device 8. Thus, a series of copying
 operations is carried out. In this case, when the developing DC bias
 applied to the developing drum 6 from the high voltage power supply 15 is
 changed, the developing conditions change. Therefore, the amount of toner
 formed on the photosensitive drum 3 changes so that it is possible to
 change the copying density.
 The optical fiber 13 is for detecting the density of the original. The
 amount of light reflected from the original is picked up by the optical
 fiber 13 so that it can be monitored as the density N of the original by
 the controller 14. The original density N, ambient temperature T and
 ambient humidity M are causes of short-term variation in the conditions of
 the electrophotographic process. The total number C of copies and the
 number D of copies made in a fixed period of time are causes of long-term
 variation in the electrophotographic process. Accordingly, the current
 value of N, T, M, C and D are monitored at all times by the controller 14.
 If the developing DC bias is controlled based upon the aforementioned
 variations, copying will always be performed at a stabilized image
 density.
 Next, the neural network 25-29 will be described.
 The input layer 25 provides analog values indicative of the values of (N,
 T, M, C, D), normalizes these values and sends them to the succeeding
 stage. The hidden layer 26, upon receiving weighting in accordance with
 weighting constants sent from the controller 14, accepts signals from the
 input layer 25. Furthermore, the output layer 17, upon receiving weighting
 in accordance with weighting constants sent from the controller 14,
 accepts signals from the hidden layer 26 and decides the firing state of
 each output element (not shown) as the result of processing based upon
 filtering threshold values. Moreover, the output layer 27 decides an
 amount of adjustment according to the set connectivity constant and
 outputs this to the high-voltage power supply 15 as an analog value.
 The bus 30 is for setting the weighting constants, which have been decided
 by the controller 14, in the sections 28 and 29 for the input and output
 connections, respectively.
 (Second Embodiment)
 Next FIG. 3 refers to a neural network for determining the image forming
 conditions and recording conditions.
 The neural network and its peripheral comprises the operation panel 16 and
 CPU 52 which changes the connectivity constants initially set to default
 conditions and be subjected to modification, and also puts out a control
 signal to the charge voltage and developing voltage. The output of the CPU
 52 is inputted to the neural network 36 to control neural units 34. This
 neural network 36 includes input layer 25, input/output connection
 (connective layer) layers 28 and 29, middle layer 26 and the output layer
 27. Concerning the neural network 36, each computing unit 34 is provided
 with a register in which the connectivity constant is stored, replaceable
 by the computer 52. The neural network multiplies the stored connectivity
 constant to the input environmental parameters, and obtain the total value
 of the computation. Then, according to the appropriate algorithm, the
 final output is obtained and puts it out to the high voltage power supply
 15. The neural network 36 consists of several neural units and is designed
 to put out the control signal for generating image generation conditions
 and recording conditions. Generally, the main function of the CPU is to
 set the connectivity constant to the register in the neural units 34 and
 replace them if required. Accordingly, if the user notifies the controller
 that recorded image density is too light or too dark, control panel 16 is
 used for input. Upon receipt of this request, the CPU 52 modify the neural
 unit 34 in neural network 36 for changing the conditions. Even if the same
 control signals are fed by CPU 52 to neural network 36, the result may be
 different. That is the output of the network becomes different and a
 different control signal is applied to the high voltage controller 15.
 Thus a different image recording may be expected. Repeating this routine
 until the user is satisfied with the recording result and upon receives
 the satisfaction that this connectivity constants is associated with, the
 input conditions are learned (inclusive of the connectivity contents) and
 stored.
 However, the range of application of the invention is not limited to the
 foregoing. For example, if the present invention is utilized in a color
 copying machine, it can be applied to correction of color tone at the time
 of copying.
 When the machine is shipped from the factory it is programmed with default
 values for the optimum connectivity constant. This constant may not give
 the best quality of copy at the installation location. Therefore means to
 adjust the constants and to adjust the quality of the copying, or a
 service person who is able to adjust the machine for that environs and
 then store the new values in ROM 14 for that environs, is provided for the
 user.
 In reference to the algorithms of FIGS. 4 and 5, the setting of new optimum
 connectivity constant and the renewal thereof will be discussed. In step
 #41, the presently stored values for the optimum connectivity constant are
 read form the CPU 52. In step #42, a copy is made using the values read
 from the CPU 52. At step #43, the user can evaluate the copy and decide
 whether or not it is acceptable. If the answer is no, then step 44 for
 changing the connectivity constant is performed. In the step, the key on
 the control panel indicating a desire to change the connectivity constant
 is pushed. Now, the user can change the connectivity constant by adjusting
 the yellow, magenta, and cyan color adjuster switches on the control panel
 of the machine. Then another copy is made in step #42, and the user is
 again posed with the question of whether or not the copy is sufficient.
 This time if the answer is yes, in step #45 the new connectivity constant
 is recorded in the neural network according to the ambient parameters at
 that time, and the copy process continues.
 In FIG. 5 the process for reading the optimum connectivity constant is
 described in a flowchart. In step 51, whether or not an old connectivity
 constant is registered is determined. If the answer is no, then the
 default value form the factory is set. If the answer is yes, then in step
 53, that value is read and used for the copy in step 54. In step 55 the
 user has the chance to examine the copy and is asked whether or not there
 is a need to adjust the connectivity constant. If the answer is no, then
 the process finishes and normal copying continues. If the answer is yes,
 then in step 56 the connectivity process is updated and in step 57 a copy
 is made from the newly input connectivity constant. In step 58 the user is
 asked whether or not he is satisfied with the new copy. If the answer is
 no, then the process returns to step 55 and begins again. If the answer is
 yes, then the new connectivity constant is stored in step 59. This
 procedure is followed when the copy machine is turned on or when the
 density change button is pushed on the control panel.
 FIG. 6 is a schematic diagram of the key locations on the operation panel.
 Key 62 is the key that the operator pushes when he is satisfied with the
 copy results. When this key is pushed the connectivity constant of that
 time is stored. This is useful to this specific embodiment of color
 copiers, because the fine tuning of the color adjustment is very
 difficult. Thus when the user is satisfied with his adjustments of the
 color he can freeze it at that point and store the connectivity constant
 into the machine. The numbers 64, 65, and 66, refer to the adjustment
 switches for the yellow, magenta, and cyan color densities respectively.
 FIG. 7 represents the preferred embodiment of the user control panel. Key
 71 determines the learning mode into which the user can change and save
 the connectivity constant, or a normal mode which can prohibit the
 learning option.
 Key 72 is the key that will clear the learning history data and return the
 default factory data. This button will be positioned in an out of the way
 location to avoid accidental clearing of the learning memory. This button
 may be used by either a service person or the user if the optimum value
 has been determined but the ambient conditions have changed requiring the
 history to be deleted and the default value of the connectivity constant
 to be used.
 Key 73 indicates a LED indicator that lights in response to an inquiry from
 controller 14 as to whether or not the data from the current run should be
 stored. The response to this query is Key 74.
 Key 74 confirms to the users an update of the running values. If the button
 is not pushed the auto learning value is registered.
 Key 75 is provided for updating the learned values at any time by the user.
 The controller is told the values when Key 75 is pushed.
 If it is assumed that a large software memory capacity is required, such as
 for back propagation, it should be ROM, and working RAM, is interfaced to
 the controller 14. According to this embodiment, an expansion memory unit
 81 is connected to the controller 14 via control lines, data bus and an
 address bus. Learning the electrographic process and optimization of such
 a process are done in the memory unit 81. Due to the utilization of the
 memory unit 81, having computing the function connected to the controller
 externally, it is possible to attain a system which has additional
 functions for performing process control by a neural network. With a
 properly designed interface, the memory unit can be designed for multiple
 uses.
 Another approach may also be possible. In this approach, a data memory for
 learning is provided in the controller 14. Learned data is accumulated
 successively in this memory. This memory is connected to the above
 mentioned memory unit 81 when a service person visits the user to update
 the optimum value with the aid of the program stored in the memory unit
 81.
 The memory unit 81 is referred to as an expansion memory unit, but it may
 be possible to realize the same by using a microcomputer board. If a
 microcomputer board is used, work allocation between the controller 14 and
 microcomputer board becomes possible.
 The controller 14 mentioned above is realized by a one-chip microcomputer
 circuit 100 which will be described hereinafter with reference to FIG. 8.
 FIG. 8 is a circuit diagram illustrating the overall arrangement of a
 microcomputer circuit 100 inclusive of the CPU 52. The CPU 52 includes
 ALU, ROM 53, RAM 54, and input/output ports for necessary circuitry
 examples. A 16 bit microcomputer device may be used for the CPU 52.
 Reference 200 denotes a neuron device such as the neuron-chip which was
 introduced in the Journal of Nikkei Microdevice of the Mar. 20, 1989
 edition as the first commercialized neuron chip which has been made by the
 bipolar process.
 In FIG. 8, a digital signal input circuit 301 stores a weighted digital
 data from the CPU 52 via a signal line 302.
 A multiplier circuit 303 multiplies the weighting data stored in the
 digital signal input circuit 301 by an analog signal from a multiplexer
 303. The multiplexer 303 selects an analog signal from the signals
 inputted through a plurality of input terminals 16, and outputs to the
 analog signal to the multiplexer 303. The product thereof is supplied to
 an adder circuit 307 via a resistance 306. The output of the adder circuit
 307 is connected to a sample and hold circuit 308 and a capacitor 309,
 which is necessary for adding operation, is connected to the outside of
 the chip 200. The signal which is held in the circuit 308 is inputted to
 the sigmoid function circuit 310, and an output signal of the sigmoid
 function circuit 310 is supplied to an output terminal 311. A control
 signal for controlling the neuron device 210 is supplied from the CPU 52
 via signal line 312. The reference numeral 314 denotes a control circuit
 which drives and controls the adder circuit 307, sample and hold circuit
 308, and sigmoid function circuit 310. A control circuit 313 for neuron
 device controls the multiplexer 304 and all of basic block 330 of the
 neuron device. As illustrated in the drawing, the CPU 52, ROM 53, RAM 54,
 I/O port 55, multiplexer 304, the neuron device control circuit 313, and
 the neuron device basic block 330 are integrated into one semiconductor
 substrate as a one-chip microcomputer.
 A control signal line 14 of FIG. 8 is outputted from the control signal
 output terminal of the CPU 52, and is supplied to the neuron device as its
 control signal 312. A control signal 9, which is supplied from the CPU 52,
 is delivered to the neuron device 200 via the signal line 302. The CPU 52
 can deliver the weighted digital data to the digital signal input circuit
 301 via the control signal line 9. The output of the sigmoid function
 circuit 310 is delivered to the CPU 52 via a signal line 7, and also is
 connected to a signal output terminal 11 of the one-chip circuit 100. The
 reference numeral 16 denotes a plurality of analog input of the neuron
 device 200, which is respectively connected to each of analog terminals of
 the multi input analog multiplexer 304. A capacitor (not shown) being
 analogous to the capacitor 309 of FIG. 8 is also connected to the adder
 circuit 307 of the neuron device 200.
 The CPU 52 provides a series of the weighted digital data to the digital
 signal input circuit 301 via the signal line 9. The weighted digital data
 is multiplied with the analog signal selected by the multiplexer 304 in
 the multiplier 303, then the product is applied to the adder circuit 309
 inclusive of a resistance 306 and the capacitor 309. The added output from
 the adder circuit 307 is supplied to the sample-hold circuit 308. Then the
 sigmoid function circuit 310 determines a level of output signal (the
 threshholding operation) corresponding to the level of signal held at the
 sample and hold circuit 308 and supplies the output signal to the signal
 line 311. In case of a plurality of analog signals are supplied to the
 multiplexor 304, the multiplexer 304 selects a desirable analog signal
 according to a selection signal 314 from the CPU 52, and the selected
 analog signal is delivered to the multiplier 303. In this situation, the
 output of the multiplier 303 is delivered to the adding circuit 303 in a
 manner of time sharing.
 According to the manner described above, the CPU 52 receives the result of
 the operation from the neuron device 200 via the signal line 7, and then
 connects it to a digital signal. The result of this arithmetic operation
 is equivalent to a result of a fuzzy reasoning made under a fuzzy control.
 When the result of the arithmetic operation is inputted, the CPU 52
 determines the state by applying the weighting data which is set at the
 digital signal input circuit 301, then control signal based on the
 determination are sent to the objects to be controlled. Of course, the CPU
 52 may change the weighting data to be set in the digital signal input
 circuit 301 through the signal line 9. The application of the information
 processing circuit illustrated in FIG. 8, which integrated with the single
 semiconductor and the neural network for controlling the setting of
 connective constants, is not necessarily limited to use for the image
 processing, but it may be possible to use for processing information for
 such goods as electric washers or refrigerators and other electric
 appliances.
 In accordance with the present invention as described above, it is possible
 to automatically adjust for differences among apparatus, changes with the
 passage of time, variations in environment, such as ambient temperature
 and humidity, and modifications in process conditions for different
 original densities without heavy burden to the controller. Moreover, since
 it is possible to alter the adjustment level depending upon the
 preferences of the user, an image which the user always finds satisfactory
 can be output stably with almost no need to perform adjustment.
 As many apparently widely different embodiments of the present invention
 can be made without departing from the spirit and scope thereof, it is to
 be understood that the invention is not limited to the specific
 embodiments thereof except as defined in the appended claims.