Abstract: An image processing method comprises obtaining an input image; converting the input image or a feature map of the input image into a plurality of target input images or target feature maps, wherein a resolution of each of the target input images or the target feature maps is smaller than a resolution of the feature map of the input image or the input image, and pixels at the same position in each of the target input images or the target feature maps are of a neighborhood relationship with the input image or the feature map of the input image; processing at least a part of the plurality of target input images or target feature maps by one or more convolution blocks in a convolutional neural network; and increasing a resolution of a feature map output from the one or more convolution blocks in the convolutional neural network.

Abstract: The disclosure provides an image optimization method, system, and storage medium. The image optimization method includes extracting texture quality information from an input image. The texture quality information indicates a spatial distribution of texture quality in the input image. The image optimization method also includes performing, according to the texture quality information, texture restoration on a set region in the input image to generate a texture restored image.

Abstract: The present disclosure relates to an image processing method and device, an electronic apparatus and a storage medium, and the image processing method includes: acquiring an input image; detecting a target area in the input image; and processing the target area, wherein the processing of the target area includes: obtaining a feature map of the target area, rearranging feature blocks in the feature map in a feature space, and obtaining an output image after the target area is processed based on the rearranged feature blocks and the feature map.

Abstract: Provided is an image processing method to process an image, the method including acquiring a plurality of channel images of the image to be processed, acquiring a noise intensity corresponding to the each of the plurality of channel images of the image to be processed, denoising the image to be processed based on the noise intensity corresponding to the each of the plurality of channel images of the image, and obtaining the denoised image.

Abstract: The disclosure provides an image optimization method, system, and storage medium. The image optimization method includes extracting texture quality information from an input image. The texture quality information indicates a spatial distribution of texture quality in the input image. The image optimization method also includes performing, according to the texture quality information, texture restoration on a set region in the input image to generate a texture restored image.

Abstract: An image processing method comprises obtaining an input image; converting the input image or a feature map of the input image into a plurality of target input images or target feature maps, wherein a resolution of each of the target input images or the target feature maps is smaller than a resolution of the feature map of the input image or the input image, and pixels at the same position in each of the target input images or the target feature maps are of a neighborhood relationship with the input image or the feature map of the input image; processing at least a part of the plurality of target input images or target feature maps by one or more convolution blocks in a convolutional neural network; and increasing a resolution of a feature map output from the one or more convolution blocks in the convolutional neural network.