Compressed multi-scale feature fusion network-based image super-resolution reconstruction method

Abstract

The invention provides a compressed multi-scale feature fusion network-based image super-resolution reconstruction method. The invention aims to solve a technical problem that a reconstructed high resolution image has a low peak signal to noise ratio and low structural similarity in the prior art. The implementation process of the invention includes the following steps that: a training sample setcomposed of high- and low-resolution image pairs is obtained; a multi-scale feature fusion network is constructed; the multi-scale feature fusion network is trained; a compressed multi-scale feature fusion network is obtained; and the compressed multi-scale feature fusion network is adopted to perform super-resolution reconstruction on an RGB image to be reconstructed. According to the compressedmulti-scale feature fusion network-based image super-resolution reconstruction method of the invention, a plurality of multi-scale feature fusion layers which are connected with one another sequentially in a stacked manner in the multi-scale feature fusion network are adopted to extract the multi-scale features of low-resolution images, and nonlinear mapping is performed on the multi-scale features of the low-resolution images; and therefore, the improvement of the low peak signal to noise ratio and low structural similarity of the reconstructed high-resolution image can be benefitted. The method can be applied to fields such as remote sensing imaging, public safety, medical diagnosis.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image super-resolution reconstruction method, in particular to an image super-resolution reconstruction method based on a compressed multi-scale feature fusion network, which can be used in remote sensing imaging, public security, medical diagnosis and other fields. Background technique [0002] Image super-resolution refers to increasing the resolution of an image. The image super-resolution reconstruction method is a method of reconstructing a corresponding high-resolution image from an observed low-resolution image. Image super-resolution reconstruction methods are mainly divided into three categories: interpolation-based, reconstruction-based, and learning-based methods. In recent years, learning-based methods have become the main research direction of image super-resolution reconstruction methods. The main idea is to use high- and low-resolution image pairs as tra...

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Problems solved by technology

[0004] The purpose of the present invention is to overcome the deficiencies in the above-mentioned prior art, and propose an image super-resolution reconstruction method based on a compressed multi-scale feature fusion network, which is used to solve the reconstructed high-resolution image peaks existing in the prior art Technical issues with low signal-to-noise ratio and structural similarity

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