Deep Regression Network for Single-Image Super-Resolution Based on Down- and Upsampling with RCA Blocks

Abstract

A regression network is created to transform low-resolution (LR) images into high-resolution (HR) images. The LR images are processed using a deep regression approach for producing HR images. LR images are initially used as input, and the raw input image is subsequently enlarged to adjust the image size without changing the information. An image’s physical size can be altered without altering the pixel proportions. After that, a regression network produces high-quality images after resizing low-quality ones. According to the simulation study, the proposed method achieves 98% accuracy, 0.02% error, 97% precision, and 94% specificity.