Image processing method and system based on image generation network model, and storage medium

Abstract

The invention relates to an image processing method and system based on an image generation network model, and a storage medium. The method comprises the following steps: pre-constructing an image generation network model comprising a perception up-sampling convolution module; sensing an up-sampling convolution module by using the feature map obtained by down-sampling; enabling first convolution channel to perform convolution of a first scale on the feature map to generate a first convolution feature map; enabling a second convolution channel to perform second-scale convolution on the featuremap to generate a second convolution feature map; enabling a fusion layer to perform fusion connection on the first convolution feature map and the second convolution feature map to generate a fusionfeature map; enabling a sub-pixel conversion layer to convert the fusion feature map into an output picture through a depth-to-space function, wherein the size of the output picture is twice that of the feature map. According to the image processing method provided by the invention, in the up-sampling process, convolution operation of two different sizes is carried out to acquire more detail features, the feature characterization of the output picture is improved, and the chessboard effect is improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image processing method, system and storage medium based on an image generation network model. Background technique [0002] There are two main purposes of shrinking an image (or called subsampling or downsampling): 1. Make the image conform to the size of the display area; 2. Generate a thumbnail of the corresponding image. The main purpose of enlarging an image (or called upsampling) is to enlarge the original image so that a higher resolution can be obtained. [0003] In the image generation model of deep learning, in order to reduce computing resources, the image to be generated usually undergoes a process of downsampling (shrinking the image) and then upsampling (restoring the reduced image). In the existing process of upsampling and restoring image size, most of them use simple upsampling layer (i.e. up-sampling) or deconvolution layer (transposeconvolution), w...

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Problems solved by technology

In the existing process of upsampling and restoring image size, most of them use simple upsampling layer (i.e. up-sampling) or deconvolution layer (transposeconvolution), which will produce serious checkerboard effect (checkerboard effect is the generated image distortion caused by Mesh

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