Single-image super-resolution reconstruction method based on lightweight neural network and Transform

Abstract

The invention discloses a single image super-resolution reconstruction method based on a lightweight neural network and a Transform. Firstly, a low-frequency feature extraction module is used for extracting a low-frequency feature map of a low-resolution image, secondly, the feature map is input into a frame formed by combining a depth separable convolution module and a Transform in a blocking mode, high-frequency texture detail features of the image are extracted, and finally, the low-frequency feature map and the high-frequency feature map are subjected to jump connection to output a high-resolution image. According to the method, super-resolution reconstruction of the low-resolution image can be realized, and the high-resolution image with rich details, clear texture and high spatial resolution is obtained.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a single image super-resolution reconstruction method based on a lightweight neural network and a Transformer. Background technique [0002] In recent years, with the vigorous development of Internet technology, images have become one of the important sources for people to obtain information. However, due to the influence of hardware performance, environmental noise, transmission method and storage method, most images will undergo a degradation process, resulting in a reduction in the quality of the image. People often cannot obtain high-resolution images. Therefore, the reconstruction of image resolution is a A research focus and difficulty in the field of image processing. Through various software algorithms to improve the resolution of the original image at low cost, it is called Super-Resolution (SR) reconstruction. Super-resolution reconstruction technology brea...

AI Technical Summary

Problems solved by technology

The self-attention mechanism in the Transformer module can effectively overcome the limitations brought by the convolution inductive bias, but at the same time, there are many parameters and a large amount of calculation. Therefore, the present invention combines the lightweight network and the Transformer model, and proposes a method based on Single Image Super-resolution Reconstruction Method with Lightweight Neural Network and Transformer

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