Progressive back-projection network for COVID-CT super-resolution. (September 2021)

• Record Type: Journal Article
• Authors: Song, Zhaoyang; Zhao, Xiaoqiang; Hui, Yongyong; Jiang, Hongmei
• Is Part Of: Computer methods and programs in biomedicine. Volume 208(2021)
• Publication Date: 2021-09
• Subjects: COVID-CT -- Super-resolution -- Progressive back-projection network -- Residual attention module -- Up-projection and down-projection residual module; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106193
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Developed a progressive back-projection network (PBPN) for COVID-CT super-resolution, which improves the performance of COVID-CT super-resolution. Designed an up-projection and down-projection residual modules (UD) to minimize the reconstruction error. Designed a residual attention module to extract deep high-frequency information. Abstract: Background and Objective: Recently, the COVID-19 epidemic has become more and more serious around the world, how to improve the image resolution of COVID-CT is a very important task. The network based on progressive upsampling for COVID-CT super-resolution increases the reconstruction error. This paper proposes a progressive back-projection network (PBPN) for COVID-CT super-resolution to solve this problem. Methods: In this paper, we propose a progressive back-projection network (PBPN) for COVID-CT super-resolution. PBPN is divided into two stages, and each stage consists of back-projection, deep feature extraction and upscaling. We design an up-projection and down-projection residual module to minimize the reconstruction error and construct a residual attention module to extract deep features. In each stage, firstly, PBPN performs back-projection to extract shallow features by two up-projection and down-projection residual modules; then, PBPN extracts deep features from the shallow features by two residual attention modules; finally, PBPN upsamples the deep features through sub-pixel convolution. Results: The proposed method achieves the improvements of about 0.14~0.47 dB/0.0012~0.0060 for × 2 scale factor, 0.02~0.08 dB/0.0024~0.0059 for × 3 scale factor, and 0.08~0.41 dB/ 0.0040~0.0147 for × 4 scale factor than state-of-the-art methods (Bicubic, SRCNN, FSRCNN, VDSR, LapSRN, DRCN and DSRN) in terms of PSNR/SSIM on benchmark datasets. Conclusions: The proposed mehtod obtains better performance for COVID-CT super-resolution and reconstructs high-quality high-resolution COVID-CT images that contain more details and edges.