Abstract: The present invention relates to a signal processing device and signal processing method, and program and recording medium, whereby images and the like closer approximating real world signals can be obtained. With regard to an input image of dimensions fewer than the dimensions of actual world light signals, wherein actual world light signals have been projected and a part of the continuity of the actual world light signals has been lost, actual world signals are estimated at an actual world signal estimating unit 10003 from the input image within a processing region set at a processing region setting unit 10001, corresponding to continuity set at a continuity setting unit 10002. On the other hand, information relating to at least one of processing region, continuity, and actual world signals, is supplied from a user I/F 10006 to the processing region setting unit 10001, continuity setting unit 10002, or actual world signal estimating unit 10003, according to user operations.

Abstract: A user interface unit sets a parameter based on operations of a user, and outputs to a parameter correction unit. The parameter correction unit extracts the feature quantity of pixels in an observation region that corresponds to the value of the parameter, and based on the maximal value of that feature quantity, corrects the parameter specified by the user as the correction parameter. A blurring correction unit corrects input image data based on the correction parameter, and generates image data of the image wherein the blurring has been corrected. The present invention can be applied, for example, to a digital camera.

Abstract: A coefficient learning apparatus includes: a student-image generation section configured to generate a student image from the teacher image; a class classification section configured to sequentially set each of pixels in the teacher image as a pixel of interest and generate a class for the pixel of interest from the values of a plurality of specific pixels; a weight computation section configured to add up feature quantities; and a processing-coefficient generation section configured to generate a prediction coefficient on the basis of a determinant including said deterioration equation and a weighted constraint condition equation.

Abstract: An image processing apparatus includes: a blur removing processing section configured to carry out a blur removing process for an input image using a plurality of blur removal coefficients for removing blur of a plurality of different blur amounts to produce a plurality of different blur removal result images; a feature detection section configured to detect a feature from each of the different blur removal result images; a blur amount class determination section configured to determine blur amount classes representative of classes of the blur amounts from the features; and a prediction processing section configured to carry out mathematic operation of pixel values of predetermined pixels of the input image and prediction coefficients learned in advance and corresponding to the blur amount classes to produce an output image from which the blur is removed.

Abstract: The present invention provides an image processing apparatus for converting a first image into a second image having higher image quality than that of the first image, includes: a first pixel value extracting section; an estimate noise amount arithmetically operating section; a processing coefficient generating section; a second pixel value extracting section; and a predicting section.

Abstract: Disclosed herein is a signal processing apparatus for carrying out signal processing to convert input data into output data with a quality higher than the quality of the input data, the data processing apparatus including: a first data extraction section; a nonlinear feature quantity computation section; a processing-coefficient generation section; a second data extraction section; and a data prediction section.

Abstract: An image processing device for converting an input image into an output image whose blur is reduced more, the image processing device including: first image extracting section configured to extract a plurality of pixels composed of a pixel of the input image which pixel corresponds to a pixel of interest as a pixel to which to direct attention within the output image and predetermined pixels surrounding the pixel of the input image; first feature quantity calculating configured to calculate a first feature quantity from the plurality of pixels extracted by the first image extracting section; processing coefficient generating section; second pixel extracting section configured to extract a plurality of pixels composed of the pixel corresponding to the pixel of interest and predetermined pixels surrounding the pixel corresponding to the pixel of interest from the input image; and predicting section.

Abstract: An image processing device and an image processing method calculate motion vectors with a small amount of calculation. A frame memory stores first and second image data, a memory includes multiple elements, and a control unit. Each element includes a pixel for storing a first pixel value of a first pixel of the first image data, a pixel for storing a second pixel value of a second pixel of the second image data, a minimal pixel difference storage unit for storing a first difference value, a motion vector storage unit for storing phase information, a comparison updating unit for comparing the first difference value stored in the minimal pixel difference storage unit, with a second difference value between the first pixel and the second pixel, updating the first difference value with the second difference value according to the results of comparison, and outputting updating information according to said updating, and an updating unit for updating the phase information according to the updating information.

Abstract: A signal processing device, signal processing method, and program and recording medium which provide images and the like closer approximating real world signals. The signal processing device includes a processing region setting unit, a movement vector setting unit, a model generator, a normal equation generator, an activity detector, a weighting change unit, and a real world estimating unit.

Abstract: A signal processing device for learning operations made by a user, and for generating signal optimal to the user based on the learning results. A learning unit monitors supplied operating signals generated based on user operations, and judges whether to learn the operating signals. When the operating signals are to be learned, the learning unit learns a correction norm for correcting input signals, based on the learning operating signals and outputs the correction norm to a correcting unit. The correcting unit corrects input signals based on the correction norm and outputs the corrected signals as output signals. The present invention can be applied to an NR (Noise Reduction) circuit which removes noise.

Abstract: A motion-vector-setting section (31) sets a first motion vector in units of pixel in a target image. An exposure-time-ratio-setting section (32) sets in units of image an exposure time ratio that is a ratio between a time interval of the target image and a period of exposure time. A motion-blur-amount-setting section (33) sets a motion blur amount in units of pixel based on the exposure time ratio and the first motion vector. Based on the motion amount, a processing-region-setting section (36) sets processing regions as well as a processing-coefficient-setting section (37) sets processing coefficients. A pixel-value-generating section (38) generates pixel values that correspond to the target pixel from pixel values in the processing region and the processing coefficients. A motion-blur-adding section (41) adds a motion blur to an image containing the pixel value generated based on an input second motion vector and the first motion vector.

Abstract: A user interface unit sets a parameter based on operations of a user, and outputs to a parameter correction unit. The parameter correction unit extracts the feature quantity of pixels in an observation region that corresponds to the value of the parameter, and based on the maximal value of that feature quantity, corrects the parameter specified by the user as the correction parameter. A blurring correction unit corrects input image data based on the correction parameter, and generates image data of the image wherein the blurring has been corrected. The present invention can be applied, for example, to a digital camera.

Abstract: A motion-vector-setting section (31) sets a motion vector in units of pixel in a target image. Based on the motion vector, a target-pixel-setting section (35) sets a target pixel for each image in plural images to be processed. A motion-blur-amount-setting section (33) sets a motion blur amount in units of pixel based on the motion vector and the exposure-time ratio set in units of image in the exposure-time-ratio-setting section (32). A processing-region-setting section (36) sets processing regions corresponding to the target pixel for each of the plural images based on the motion blur amount. A processing-coefficient-setting section (37) sets processing coefficients based on the motion blur amount.

Abstract: A shooting-information-detecting section (31) detects shooting information from an image pick-up section (10). A motion-detecting section (33) detects a motion direction of an image on an overall screen based on a motion direction of the image pick-up section contained in the shooting information. A processing-region-setting section (36) sets a processing region in at least any one of a predicted target image and a peripheral image thereof, which correspond to a target pixel in the predicted target image. A processing-coefficient-setting section (37) sets a motion-blur-removing-processing coefficient that corresponds to the motion direction detected in the motion-detecting section (33). A pixel-value-generating section (38) generates a pixel value that corresponds to the target pixel based on a pixel value of a pixel in the processing region set in the processing-region-setting section (36) and the processing coefficient set in the processing-coefficient-setting section (37).

Abstract: A motion-setting section (61) sets a motion amount and a motion direction for obtaining processing coefficients. A student-image-generating section (62) generates student images obtained by adding a motion blur to a teacher image not only based on the set motion amount and the set motion direction but also by changing at least one of the motion amount and motion direction in a specific ratio and student images obtained by adding no motion blur to the teacher image. A prediction-tap-extracting section (64) extracts, in order to extract a main term that mainly contains component of the target pixel, at least a pixel value of pixel in the student image whose space position roughly agrees with space position of the target pixel in the teacher image.

Abstract: An image processing apparatus includes an object extracting unit, a selecting unit, and an object storing unit. The object extracting unit extracts an object from a target image through a plurality of processing, and outputs the result of extracting the object. The selecting unit selects at least a part of the result of extracting the object which is obtained through the plurality of processing, in accordance with a user's operation. The object storing means stores the result of extracting the object which is selected by the selecting unit.

Abstract: A target-pixel-setting section (31) sets a target pixel in a target image to be predicted. A motion-direction-detecting section (32) detects a motion direction corresponding to the target pixel. A pixel-value-extracting section (36) extracts from peripheral images corresponding to the target image, in order to extract a main term that mainly contains component of the target pixel in a moving object that encounters a motion blur in the peripheral images, at least pixel values of pixels in the peripheral images whose space position roughly agree with space position of the target pixel. A processing-coefficient-setting section (37a) sets a specific motion-blur-removing-processing coefficient.

Abstract: A signal processing device, signal processing method, and program and recording medium which provide images and the like closer approximating real world signals. The signal processing device includes a processing region setting unit, a motion vector setting unit, a model generator, a normal equation generator, a weighting change unit, and a real world estimating unit.

Abstract: A signal processing device, signal processing method, and program and recording medium which provide images and the like closer approximating real world signals. The signal processing device includes a processing region setting unit, a movement vector setting unit, a model generator, a normal equation generator, an activity detector, a weighting change unit, and a real world estimating unit.

Abstract: The present invention relates to a signal processing device and signal processing method, and program and recording medium, whereby images and the like closer approximating real world signals can be obtained. With regard to an input image of dimensions fewer than the dimensions of actual world light signals, wherein actual world light signals have been projected and a part of the continuity of the actual world light signals has been lost, actual world signals are estimated at an actual world signal estimating unit 10003 from the input image within a processing region set at a processing region setting unit 10001, corresponding to continuity set at a continuity setting unit 10002. On the other hand, information relating to at least one of processing region, continuity, and actual world signals, is supplied from a user I/F 10006 to the processing region setting unit 10001, continuity setting unit 10002, or actual world signal estimating unit 10003, according to user operations.