An image scaling processor is disclosed comprising a multi-pixel vertical scaler and a multiline horizontal scaler. The multi-pixel vertical scaler includes a plurality of convolvers that convolve a plurality of pixels in parallel with a vertical kernel comprising weights to generate vertically scaled pixels. The multiline horizontal scaler includes horizontal scaling circuits configured to receive vertically scaled pixels from respective line stores, wherein a given horizontal scaling circuit comprises an input configured to receive one vertically scaled pixel at a time, a pipeline of convolver stages configured to, at each stage, convolve vertically scaled pixels with respective horizontal kernel coefficients. The pipeline transfers pixels from a last convolver stage of the pipeline to a second memory, wherein the pixels from the last convolver stage of the pipeline are horizontally and vertically scaled, wherein the pixels stored in the second memory are arranged to be displayed as a scaled image.

An inverse matrix iterative deconvolution method for spectral resolution enhancement comprises the following steps: step 1, sequence convolution and convolution square matrices; step 2, cumulative multiplication of convolution square matrices and convolution kernel function peak broadening; and step 3, peak resolution enhancement. The invention achieves the purpose of narrowing the peak width by multiplying primitive functions by deconvolution matrices. Further, the invention provides a method for constructing a deconvolution identity matrix to achieve the deconvolution effect with an expected precision. The calculation process is fast and controllable with stable and accurate results, and the application range is wide. The method can be used for resolution enhancement of molecular spectra such as Raman and infrared spectra, as well as other spectra with symmetrical peak patterns such as mass spectra, nuclear magnetic resonance, XRD and XRF.

An image processing method includes a first step of acquiring input data including a captured image and optical system information relating to a state of an optical system used for capturing the captured image and a second step of inputting the input data to a machine learning model and of generating an estimated image acquired by sharpening the captured image or by reshaping blurs included in the captured image.

A video coder is provided for predictive coding, into an encoded video bit stream, of an original block of an original frame based on a prediction block obtained from a reference frame, comprising a buffer configured to store the original block, a buffer configured to store the prediction block, and an adaptive sharpening filter configured to apply a spatial adaptive sharpening filtering to the prediction block.

A method for restoring images in a sequence of images, including, when it is applied to a first image in the image sequence: estimating an item of information representing a global motion of a background of the first image with respect to a second image; compensating for said global motion of the background in the second image in order to obtain an adjusted version of the second image, referred to as the adjusted second image; obtaining a contour of an object of the first image by applying a segmentation method using the adjusted second image; using the contour of the object thus obtained in order to estimate an item of information representing a global motion of the object; and applying to the first image an image restoration method using the information representing the estimated global motion of the background and the estimated global motion of the object.

An image grid line removing method is used for removing grid lines of an image and includes a Fourier transform step, a Gaussian-like masking step and an inverse Fourier transform step. The Fourier transform step is for providing a Fourier transform to process the image to generate a frequency domain image. The Gaussian-like masking step is for providing a Gaussian-like masking model to process the frequency domain image to generate a frequency domain masked image. The inverse Fourier transform step is for providing an inverse Fourier transform to process the frequency domain masked image to generate a grid line removing image.

An image processing apparatus comprises an acquisition unit configured to acquire image data, a first tone correction unit configured to estimate transparency for each region of the image data, and correct tone, a second tone correction unit configured to correct tone of the image data using a tone curve, and a control unit configured to control which tone correction unit is to be used to perform tone correction.

A device includes a feature amount calculating unit for receiving an infrared light image and a visible light image and extracting a feature amount from at least one of the images and an image correcting unit for executing pixel value correction processing on the infrared light image on the basis of a reference area and a correction parameter determined depending on the feature amount. The device further includes a tap selection unit for determining the reference area used for the pixel value correction on the basis of the feature amount and a correction parameter calculating unit for determining the correction parameter used for the pixel value correction on the basis of the feature amount. The image correcting unit executes the pixel value correction processing in which a tap determined by the tap selection unit and the correction parameter determined by the correction parameter calculating unit are applied.

A method and an apparatus for a complete size adaptation of an object to be displayed are provided. The method includes obtaining elements and description information of the elements included in an object to be displayed, the description information of the elements including information of parent-child relationships among the elements and rules for adaptation into display areas of different sizes based on the parent-child relationships; determining sizes and positions of the elements that are included in the object to displayed and are adapted to a current display area based on the elements and the description information of the elements that are obtained and size information of the current display area; and displaying the elements included in the object to be displayed in the current display area using the sizes and the positions that are determined.

The image processing apparatus includes: an input unit configured to receive input of a plurality of images; an alignment unit configured to select each of the plurality of images one by one as a base image, and aligns the base image and each of comparison images; a selection unit configured to select the comparison image to be combined with the base image from among the comparison images aligned with the base image for each of the base images, calculate a common region in which images included in the combination can be commonly drawn, and select the combination in which the ratio of the common region to the base image satisfies a predetermined condition; a determination unit configured to determine the combination that maximizes the number of comparison images from the selected combination; and an output unit configured to output an image group corresponding to the determined combination.