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Progressive back-projection network for COVID-CT super-resolution.
- Source :
-
Computer Methods & Programs in Biomedicine . Sep2021, Vol. 208, pN.PAG-N.PAG. 1p. - Publication Year :
- 2021
-
Abstract
- • 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. 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. 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. 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. 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *COVID-19 pandemic
*PROBLEM solving
*FEATURE extraction
*PIXELS
Subjects
Details
- Language :
- English
- ISSN :
- 01692607
- Volume :
- 208
- Database :
- Academic Search Index
- Journal :
- Computer Methods & Programs in Biomedicine
- Publication Type :
- Academic Journal
- Accession number :
- 151814535
- Full Text :
- https://doi.org/10.1016/j.cmpb.2021.106193