Title: Research on application of spatial attention mechanism in the super-resolution reconstruction of single-channel greyscale image of mouse brain

Authors: Yanan Wang; Yi Luo; Jia Ren

Addresses: Network Information Management Centre, Shandong University of Art & Design, Shan Dong Sheng, Jinan 250030, China ' School of Information and Communication Engineering, Hainan University, Haikou 570228, Hainan, China ' School of Information and Communication Engineering, Hainan University, Haikou 570228, Hainan, China; Research Institute of Shipbuilding Industry Design, Shandong University of Art & Design, Shan Dong Sheng, Jinan 250030, China

Abstract: High-resolution medical images are an essential basis for scientific research and clinical judgment. At this stage, saq is still limited by the hardware conditions of imaging equipment and the generalisation of imaging methods. An economical method would involve the application of Single Image Super-Resolution (SISR) technology in the acquisition of high-resolution images in the biomedical field. Based on the classic super-resolution network, the cascaded residual plate aimed at CARN is examined into in this study. Regarding the problem of the insufficient feature learning process, the sSE module of spatial incentives was introduced, and the network structure of CARN was optimised. The mouse brain nerve image reconstructed by the CARN-L algorithm is significantly improved compared with the original image.

Keywords: medical image; image super-resolution; deep learning; cascade residuals.

DOI: 10.1504/IJWMC.2022.124816

International Journal of Wireless and Mobile Computing, 2022 Vol.22 No.3/4, pp.259 - 264

Received: 29 Sep 2021
Accepted: 27 Feb 2022

Published online: 09 Aug 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article