Abstract: There is provided an image processing apparatus including a processing unit configured to superimpose a plurality of X-ray images that are based on X-ray detection data representing detection results obtained by detecting a plurality of times in a time-division manner parallel beam X-rays output from a ray source including a plurality of X-ray sources that output parallel beam X-rays.

Abstract: There is provided an image processing apparatus including a processing unit configured to processes projection data in which X-ray detection data representing a detection result of parallel beam X-rays output from an X-ray source has been converted by projection, and form an X-ray image based on the X-ray detection data.

Abstract: There is provided an information processing apparatus, including a first processing unit capable of processing an image, a second processing unit capable of processing the image in parallel for each unit dividing the image, and a controller section configured to perform a control to select one of the first processing unit, the second processing unit, and both of them as a subject or subjects processing the image, to divide, in a case where both the first processing unit and the second processing unit are selected, the image into a first region and a second region, and to assign processing of an image of the first region and processing of an image of the second region, which are obtained by the division, to the first processing unit and the second processing unit, respectively, to cause the first processing unit and the second processing unit to perform the processing.

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 for converting a first image into a second image having higher image quality than the first image. The image processing apparatus including first pixel value extracting means for extracting plural pixel values within the first image and estimate noise amount arithmetically operating means for obtaining estimate noise amounts for the plural pixel values. The image processing apparatus also including processing coefficient generating means for generating second processing coefficients in accordance with an arithmetic operation for first processing coefficients and the estimate noise amounts, second pixel value extracting means for extracting a plurality of pixel values, and predicting means for generating a pixel value of the pixel of interest.

Abstract: An image processing device for converting an input image into an output image whose blur is reduced. The image processing device includes a first image extracting section that extracts a plurality of pixels composed of a pixel of the input image corresponding to a pixel of interest in the output image and predetermined pixels surrounding the pixel of the input image. A first feature quantity calculating section calculates a first feature quantity from the plurality of pixels extracted by the first image extracting section. A second pixel extracting section extracts a plurality of pixels composed of the pixel of interest and predetermined pixels surrounding the pixel of interest from the input image. A predicting section then generates a pixel value of the pixel of interest.

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: 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: 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: There is provided an information processing apparatus, including a first processing unit capable of processing an image, a second processing unit capable of processing the image in parallel for each unit dividing the image, and a controller section configured to perform a control to select one of the first processing unit, the second processing unit, and both of them as a subject or subjects processing the image, to divide, in a case where both the first processing unit and the second processing unit are selected, the image into a first region and a second region, and to assign processing of an image of the first region and processing of an image of the second region, which are obtained by the division, to the first processing unit and the second processing unit, respectively, to cause the first processing unit and the second processing unit to perform the processing.

Abstract: An information processing apparatus includes a graphics processing unit capable of dividing processing on an image into a plurality of threads, and executing processing on the image, a determining section configured to search for a thread parameter with which the graphics processing unit is capable of executing processing at the highest speed under a given image-processing condition, and to determine the thread parameter as an optimum thread parameter, a transferring section configured to establish a correspondence between the image-processing condition and the optimum thread parameter determined by the determining section, and to accumulate the image-processing condition and the optimum thread parameter in a database via a transmission line, and a setting section configured to obtain the optimum thread parameter from the database via the transmission line, and to set the optimum thread parameter to the graphics processing unit.

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: 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: The present invention relates to a signal processing device which learns operations made by a user without the user knowing, and which can perform processing optimal to the user based on the learning results. At a learning unit 22, operating signals supplied according to user operations are monitored, and judgment is made whether or not these can be used for learning. In the event that the operating signals are learning operating signals which can be used for learning, the learning unit 22 learns a correction norm which is the norm for correcting input signals, based on the learning operating signals. On the other hand, at a correcting unit 21, post-correction signals, wherein the input signals are corrected based on the correction norm obtained by learning, are output as output signals. The present invention can be applied to an NR (Noise Reduction) circuit which removes noise.

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 shooting-information-detecting section detects shooting information from an image pick-up section. A motion-detecting section 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 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 sets a motion-blur-removing-processing coefficient that corresponds to the motion direction detected in the motion-detecting section. A pixel-value-generating section 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 and the processing coefficient set in the processing-coefficient-setting section. Motion-blur-removing processing can be accurately performed.

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: An image signal processing apparatus and method perform appropriate correction of image blurring in accordance with an image characteristic. In the apparatus, a user interface designates a blurring parameter, and a control signal generating unit generates a control signal corresponding to the designated parameter. An image characteristic detecting unit determines directions in which pixels in an input image have flat levels and directions in which pixels in the input image have levels corresponding to edges. Based on the determination, an address calculating unit reads coefficients from a coefficient ROM and supplies the coefficients to a product-sum calculating unit. The product-sum calculating unit generates a blurring-eliminated image by performing product-sum calculation using the coefficients. A post-processing unit produces an output image based on the input image and the result of product-sum calculation.

Abstract: 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. An object which is moving at a movement amount v in the horizontal direction is photographed, and an image wherein the object is blurred is input into a signal processing device. A continuity setting unit supplies the movement amount v of the object to an actual world estimating unit as continuity information. The actual world estimating unit estimates a pixel value for an image without blurring, by computing a normal equation comprising a model equation which models the relation of the pixel values in the input image and the pixel values in an image without blurring according to the movement amount v, and a constraint condition expression which constrains between the pixels in an image without blurring, and supplies this to an image generating 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 motion vector setting unit, a model generator, a normal equation generator, a weighting change unit, and a real world estimating unit.