A super-resolution network using channel attention retention for pathology images

ABSTRACT: Image super-resolution (SR) significantly improves the quality of low-resolution images, and is widely used for image reconstruction in various fields. Although the existing SR methods have achieved distinguished results in objective metrics, most methods focus on real-world images and employ large and complex network structures, which are inefficient for medical diagnosis scenarios. To address the aforementioned issues, the distinction between pathology images and real-world images was investigated, and an SR Network with a wider and deeper attention module called Channel Attention Retention is proposed to obtain SR images with enhanced high-frequency features. This network captures contextual information within and across blocks via residual skips and balances the performance and efficie

SUBMITTER: Jia F 

PROVIDER: S-EPMC10280234 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature