Deep RegNet-150 architecture for single image super resolution of real-time unpaired image data

Abstract

Single Image Super-Resolution (SISR) is a fundamental computer vision task aimed at enhancing the spatial resolution and quality of low-resolution images. In recent years, deep learning techniques have revolutionized the field of SISR, enabling remarkable advancements in image enhancement. Typically, the Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) based deep learning techniques are employed for the SISR. However, the existing CNN and GAN architectures lag in providing maximum performance in terms of SSIM and PSNR. In this paper, a regression deep learning architecture is designed for the SISR. The proposed Deep Regression Network with 150 (Deep RegNet-150) is designed as a U-net. The proposed Deep RegNet-150 includes up-sampling and down-sampling portions with Residual Channel Attention Block (RCAB) for SISR. The RCAB acts as the building block of image super resolution (SR) by including ReLU and Conv layer. The performance of the proposed technique is evaluated on different image datasets, including a text image dataset and an unpaired dataset. The performance in terms of SSIM and PSNR for 4X and 8X SR is evaluated. The proposed Deep RegNet-150 attained maximum PSNR / SSIM values of 43.99 / 0.989 and 42.73 / 0.987 for 4X and 8X SR of the benchmark dataset, respectively. The 4X and 8X SR of unpaired datasets attained 46.13 / 0.991 and 45.97 / 0.990 of PSNR /SSIM, respectively. Ultimately, it proves that the Deep RegNet-150 is an effective deep learning architecture for the SISR.