An image super-resolution method based on a channel attention mechanism and multilayer feature fusion

Abstract

The invention relates to an image super-resolution method based on a channel attention mechanism and multilayer feature fusion, and the method comprises the steps of directly extracting the original features of a low-resolution image at the beginning of a residual branch by using a single-layer convolutional layer based on deep learning; using six cascaded convolutional circulation units based ona channel attention mechanism and multi-layer feature fusion to extract accurate depth features; carrying out upsampling on the depth features through a deconvolution layer, and carrying out dimensionality reduction on the upsampled features through a single-layer convolution layer to obtain a residual error of the high-resolution image; carrying out up-sampling on the low-resolution image by using a bicubic interpolation method in a mapping branch to obtain mapping of the high-resolution image; and adding the mapping and the residual of the high-resolution image pixel by pixel to obtain a final high-resolution image. The method is reasonable in design, fully considers the difference between the feature channels, efficiently utilizes the hierarchical features, and maintains a higher operation speed while obtaining higher accuracy.

Description

technical field [0001] The invention belongs to the technical field of computer vision images, in particular to an image super-resolution method based on channel attention mechanism and multi-layer feature fusion. Background technique [0002] With the advancement of science and technology, more and more image resolution formats have appeared in people's daily life, from "Standard Definition" (Standard Definition) to "High Definition" (High Definition), and then to the current common " 1080p", "2K" or even "4K" and so on. Higher resolution means that more image details are included, which in turn means that there may be a greater amount of information, and a greater amount of information implies greater application potential. However, in the real world, on the one hand, limited by the physical performance of imaging equipment, people cannot obtain high-resolution images; on the other hand, in Internet applications, limited by network bandwidth and storage medium capacity, u...

AI Technical Summary

Problems solved by technology

However, none of these methods take into account the different importance among feature channels, or fail to make full use of hierarchical features, resulting in more limited results.

Images