Abstract: A coefficient learning apparatus includes a regression coefficient calculation unit configured to obtain a tap from an image of a first signal; a regression prediction value calculation unit configured to perform a regression prediction computation; a discrimination information assigning unit configured to assign discrimination information to the pixel of interest; a discrimination coefficient calculation unit configured to obtain a tap from the image of the first signal; a discrimination prediction value calculation unit configured to perform a discrimination prediction computation; and a classification unit configured to classify each of the pixels of the image of the first signal into one of the first discrimination class and the second discrimination class.

Abstract: An image processing apparatus includes a storage unit in which regression coefficient data is stored for each class on the basis of a tap in which a linear feature amount corresponding to a pixel of interest of first image data and a non-linear feature amount determined from the image data are used as elements; a classification unit configured to classify each of linear feature amounts of a plurality of items of input data of the input first image into a predetermined class; a reading unit configured to read the regression coefficient data; and a data generation unit configured to generate data of a second image obtained by making the first image have higher quality by performing a product-sum computation process by using the regression coefficient data read from the reading unit and elements of the tap of each of the plurality of items of input data of the input first image.

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: Provided is a failure recovery method for switching to a standby system if a failure occurs in an active system, which allows reduction of loads on the standby system and a network. A computer system, which receives data to which timestamp information is appended in a chronological order and processes the received data by registered queries, includes the active system for processing the received data and the standby system for processing the received data instead of the active system upon occurrence of a failure. The active system processes the received data by the respective queries according to a predefined sequence, and transmits results obtained from the received data by the respective queries to the standby system as intermediate data on a query basis at a predetermined timing. The standby system restores the data by processing the received data and the intermediate data if a failure occurs in the active system.

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: There is provided an image processing apparatus including a calculation part calculating a prediction value of a target pixel in an image captured by an image capturing part capturing images using an image sensor configured by regularly arranging a plurality of pixels having a plurality of exposure times based on values of a plurality of other pixels different from the target pixel in exposure time and prediction coefficients corresponding to the respective other pixels; and a motion amount identifying part identifying a motion amount of the target pixel per unit time based on the calculated prediction value of the target pixel and a value of the target pixel.

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 prediction calculation unit calculates a pixel value of a pixel of interest for each color component by a calculation of a learned predictive coefficient and a predictive tap, and outputs an output image including the pixel value of the pixel of interest of each color component. For example, the present technology can be applied to an image processing apparatus.

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 coefficient learning apparatus includes a regression coefficient calculation unit for calculating a regression coefficient, a regression prediction value calculation unit for calculating a regression prediction value, a discrimination information assigning unit for assigning discrimination information for discriminating whether a target pixel belongs to a first discrimination class or a second discrimination class, a discrimination coefficient calculation unit for calculating a discrimination coefficient, a discrimination prediction value calculation unit for calculating a discrimination prediction value, and a classification unit for classifying the pixels of the image of the first signal into any one of the first discrimination class and the second discrimination class based on the calculated discrimination prediction value.

Abstract: An image processing apparatus includes a parameter input unit, a tap extraction unit, a predictive coefficient calculation unit, a predictive coefficient calculation unit, and a pixel value operation unit. The parameter input unit receives a parameter including an output phase, the size of an output pixel, and a variable used for a condensing model. The tap extraction unit extracts a tap including a pixel value of a focus pixel which corresponds to the output phase and pixel values of neighboring pixels of the focus pixel. The predictive coefficient calculation unit calculates a predictive coefficient to be multiplied by each of the elements of the tap. The pixel value operation unit calculates a value of the output pixel by performing a product-sum operation of the calculated predictive coefficient and each of the elements of the tap.

Abstract: There is provided a method executed in a cluster system comprising a first computer and at least one second computer that stores the data transmitted from the first computer. The method comprising the steps of: judging whether the failure-occurred processing is restarted by the first computer or the failure-occurred processing is switched over by the second computer based on the system information; receiving the stored data from the second computer and re-executing the processing in which the failure has occurred by the first computer, in the case of which it is judged that the processing in which the failure has occurred is restarted by the first computer; and executing, by the second computer, the processing in which the failure has occurred, in the case of which it is judged that the processing in which the failure has occurred is switched over by the at least one second computer.

Abstract: An information processing apparatus includes an image taking means for taking images of a subject, a class-classification means for classifying a first image outputted by the image taking means into a class according to a characteristics thereof, a storage means for storing plural coefficient memories having different image taking conditions at the time of image taking, which store prediction coefficients according to the class acquired by learning, a designation means for designating one coefficient memory from among the plural coefficient memories based on the image taking condition when the image was taken by the image taking means, and a calculation means for calculating a second image in which noise is removed from the first image by calculating a prediction coefficient of the class of the first image, which is in the designated coefficient memory.

Abstract: Provided is a failure recovery method for switching to a standby system if a failure occurs in an active system, which allows reduction of loads on the standby system and a network. A computer system, which receives data to which timestamp information is appended in a chronological order and processes the received data by registered queries, includes the active system for processing the received data and the standby system for processing the received data instead of the active system upon occurrence of a failure. The active system processes the received data by the respective queries according to a predefined sequence, and transmits results obtained from the received data by the respective queries to the standby system as intermediate data on a query basis at a predetermined timing. The standby system restores the data by processing the received data and the intermediate data if a failure occurs in the active system.

Abstract: An image processing apparatus includes a storage unit in which regression coefficient data is stored for each class on the basis of a tap in which a linear feature amount corresponding to a pixel of interest of first image data and a non-linear feature amount determined from the image data are used as elements; a classification unit configured to classify each of linear feature amounts of a plurality of items of input data of the input first image into a predetermined class; a reading unit configured to read the regression coefficient data; and a data generation unit configured to generate data of a second image obtained by making the first image have higher quality by performing a product-sum computation process by using the regression coefficient data read from the reading unit and elements of the tap of each of the plurality of items of input data of the input first image.