A pixel value calibration method includes: obtaining input image data generated by pixels, the input image data including a first group of pixel values in a first color plane and a second group of pixel values in a second color plane, generated by a first portion and a second portion of the pixels respectively; determining a difference function associated with filter response values and target values, the filter response values being generated by utilizing characteristic filter coefficients to filter first and second estimated pixel values of estimated pixel data in the first and second color planes, respectively; determining a set of calibration filter coefficients by calculating a solution of the estimated pixel data, the solution resulting in a minimum value of the difference function; and filtering the input image data, by a filter circuit using the set of calibration filter coefficients, to calibrate the first group of pixel values.

A method includes obtaining an input image that contains a particular representation of lens flare, and processing the input image by a machine learning model to generate a de-flared image that includes the input image with at least part of the particular representation of lens flare removed. The machine learning (ML) model may be trained by generating training images that combine respective baseline images with corresponding lens flare images. For each respective training image, a modified image may be determined by processing the respective training image by the ML model, and a loss value may be determined based on a loss function comparing the modified image to a corresponding baseline image used to generate the respective training image. Parameters of the ML model may be adjusted based on the loss value determined for each respective training image and the loss function.

A method for using a single-pixel camera to reconstruct images of objects obscured by fog or other dynamic scattering media. A pseudo-random phase or intensity pattern is imposed on illumination beams directed at a target. The beam with the imposed pattern forms a pseudo random pattern on the target. Information regarding the pattern imposed on each pulse is entered into a data processor/controller. The illumination beams with the pseudo random patterns are reflected off the target, collected by receiving optics and a bucket detector and converted into electronic signals fed into the data processor/controller. The data processor/controller applies a high-pass filter to remove slower signal variations produced by dynamic changes in the scattering medium over time. The filtered bucket values are then used together with their corresponding speckle patterns to generate the images using any appropriate reconstruction algorithm such as CGI or CSI.

A method for correcting an image divides an output image into a grid with vertical sections of width smaller than the image width but wide enough to allow efficient bursts when writing distortion corrected line sections into memory. A distortion correction engine includes a relatively small amount of memory for an input image buffer but without requiring unduly complex control. The input image buffer accommodates enough lines of an input image to cover the distortion of a single most vertically distorted line section of the input image. The memory required for the input image buffer can be significantly less than would be required to store all the lines of a distorted input image spanning a maximal distortion of a complete line within the input image.

A method for calibrating defective channels of a CT device involves in a step S10, acquiring original data collected by the CT device; in a step S20, capturing to-be-recovered areas from the original data, wherein the to-be-recovered areas contain the defective channels of the CT device; in a step S30, inputting data of the to-be-recovered areas to a neural network for training so as to generate training results; and in a step S40, using the training results to repair the to-be-recovered areas. The method eliminates effects of artifacts caused by defective channels on image reconstruction.

An image processing apparatus decodes encoded RAW data that includes subband data being encoded with lossy encoding scheme, and determines one of a plurality of classifications based on the decoded subband data, wherein the plurality of classifications are based on a feature of an image. The apparatus also obtains correction data corresponding to the determined classification, and corrects recomposed data, which is obtained by applying frequency recomposition to the decoded subband data, based on the correction data, in order to obtain the corrected data as decoded RAW data.

The present disclosure discloses a method and a system for generating a composite PET-CT image based on a non-attenuation-corrected PET image. The method includes: constructing a first generative adversarial network and a second generative adversarial network; obtaining a mapping relationship between a non-attenuation-corrected PET image and an attenuation-corrected PET image by training the first generative adversarial network; obtaining a mapping relationship between the attenuation-corrected PET image and a CT image by training the second generative adversarial network; and generating the composite PET-CT image by utilizing the obtained mapping relationships. According to the present disclosure, a high-quality PET-CT image can be directly composited from a non-attenuation-corrected PET image, and medical costs can be reduced for patients, and radiation doses applied to the patients in examination processes can be minimized.

An X-ray diagnosis apparatus according to an embodiment includes processing circuitry configured: to detect an element from X-ray image data taken of an examined subject; to determine a parameter of multi-frequency processing on the basis of a detection result of the element; and to execute the multi-frequency processing on one or both of the X-ray image data and another piece of X-ray image data taken later than the X-ray image data, on the basis of the determined parameter.

Disclosed are a video processing method and a device therefor. The video processing method may include receiving a video comprising a plurality of temporal portions, receiving a first model parameter corresponding to a first neural network to process the video entirely, receiving residues between the first model parameter and a plurality of second model parameters corresponding to a plurality of second neural networks to process the plurality of temporal portions, and performing at least one of super-resolution, reverse or inverse tone mapping, tone mapping, frame interpolation, motion deblurring, denoising, and compression artifact removal on the video based on the residues.

An image processing method and apparatus based on machine learning are disclosed. The image processing method based on machine learning, according to the present invention, may comprise the steps of: generating a first corrected image by inputting an input image to a first convolution neural network; generating an intermediate image on the basis of the input image; performing machine learning on a first loss function of the first convolution neural network on the basis of the first corrected image and the intermediate image; and performing machine learning on a second loss function of the first convolution neural network on the basis of the first corrected image and a natural image.