Abstract: An image processing method includes obtaining a difference map of an original image, obtaining an edge feature parameter of a first pixel in an intermediate image based on a pixel value of a pixel in the difference map, obtaining an edge feature parameter of a second pixel in a target image based on the edge feature parameter, and obtaining the target image based on the edge feature parameter of the second pixel.

Abstract: A neural network model of M network layers, a data processing method, and a processing apparatus configured to execute N tasks, where an ith network layer has a shared weight value to execute each of the N tasks and N groups of dedicated weight values, where each of the N groups of dedicated weight values executes one of the N tasks, all the groups of dedicated weight values are in a one-to-one correspondence with the N tasks, M is a positive integer and 1?i?M, when executing a first task, the ith network layer is configured to obtain input data, obtain output data based on a tth group of dedicated weight values, the shared weight value, and the input data, when 1?i?M, transmit the output data to an (i+1)th network, where the tth group of dedicated weight values corresponds to the first task, and when i=M, output the output data.

Abstract: A sub-pixel displacement measurement method based on a Tikhonov regularization, including the following steps: collecting two images before a structure is deformed, and recording the two images as reference images; collecting an image after the structure is deformed, and recording the image as a target image; extracting grayscale matrices in the two reference images, recording the grayscale matrices as f0 and f1, and calculating a noise level parameter ? of the two reference images: taking a pixel point to be measured as a center point, extracting a square region in the target image, recording a grayscale matrix of the square region as g, and using a Tikhonov regularization method to separately obtain grayscale gradient matrices of the square region along an x direction and along a y direction; and calculating a sub-pixel displacement of the structure by using the grayscale gradient matrices.

Abstract: An image processing method includes obtaining a difference map of an original image, obtaining an edge feature parameter of a first pixel in an intermediate image based on a pixel value of a pixel in the difference map, obtaining an edge feature parameter of a second pixel in a target image based on the edge feature parameter, and obtaining the target image based on the edge feature parameter of the second pixel.

Abstract: Embodiments of the present invention provide an image processing method and apparatus, where the image processing method includes: performing image magnification processing on a low-resolution image to obtain an initial high-resolution image; selecting a central similar block corresponding to a first pixel from the initial high-resolution image; selecting a search block corresponding to the first pixel from the low-resolution image, and determining each similar block in the search block, where the central similar block and the search block have a same block size; and determining a pixel value of the first pixel according to the central similar block and each similar block in the search block. By using technical solutions in the embodiments of the present invention, calculation complexity and an occupied memory resource can be effectively reduced.

Abstract: A super-resolution method and apparatus for a video image are disclosed. The method includes performing super-resolution processing based on fuzzy motion estimation by integral multiple times on an original low-resolution video image, to obtain an intermediate high-resolution video image. When resolution of the intermediate high-resolution video image is different from resolution of a target high-resolution video image, a non-integral multiple between the resolution of the target high-resolution video image and the resolution of the intermediate high-resolution video image is calculated. Super-resolution processing based on interpolation by the non-integral is performed multiple times on the intermediate high-resolution video image, to obtain the target high-resolution video image.

Abstract: Embodiments of the present invention provide an image processing method and apparatus, where the image processing method includes: performing image magnification processing on a low-resolution image to obtain an initial high-resolution image; selecting a central similar block corresponding to a first pixel from the initial high-resolution image; selecting a search block corresponding to the first pixel from the low-resolution image, and determining each similar block in the search block, where the central similar block and the search block have a same block size; and determining a pixel value of the first pixel according to the central similar block and each similar block in the search block. By using technical solutions in the embodiments of the present invention, calculation complexity and an occupied memory resource can be effectively reduced.

Abstract: A super-resolution method and apparatus for a video image are disclosed. The method includes performing super-resolution processing based on fuzzy motion estimation by integral multiple times on an original low-resolution video image, to obtain an intermediate high-resolution video image. When resolution of the intermediate high-resolution video image is different from resolution of a target high-resolution video image, a non-integral multiple between the resolution of the target high-resolution video image and the resolution of the intermediate high-resolution video image is calculated. Super-resolution processing based on interpolation by the non-integral is performed multiple times on the intermediate high-resolution video image, to obtain the target high-resolution video image.