Deep Iterative Residual Convolutional Network for Single Image Super-Resolution

Deep convolutional neural networks (CNNs) have recently achieved great success for single image super-resolution (SISR) task due to their powerful feature representation capabilities. The most recent deep learning based SISR methods focus on designing deeper / wider models to learn the non-linear mapping between low-resolution (LR) inputs and high-resolution (HR) outputs. These existing SR methods do not take into account the image observation (physical) model and thus require a large number of network's trainable parameters with a great volume of training data. To address these issues, we propose a deep Iterative Super-Resolution Residual Convolutional Network (ISRResCNet) that exploits the powerful image regularization and large-scale optimization techniques by training the deep network in an iterative manner with a residual learning approach. Extensive experimental results on various super-resolution benchmarks demonstrate that our method with a few trainable parameters improves the results for different scaling factors in comparison with the state-of-art methods.

I. Introduction

The goal of the single image super-resolution (SISR) is to recover the high-resolution (HR) image from its low-resolution (LR) counterpart. SISR problem is a fundamental low-level vision and image processing problem [1], [2] with various practical applications in satellite imaging, medical imaging, astronomy, microscopy, seismology, remote sensing, surveillance, biometric, image compression, etc. In the last decade, most of the photos are taken using built-in smart-phones cameras, where the resulting LR image is inevitable and undesirable due to their physical limitations. It is of great interest to restore sharp HR images because some captured moments are difficult to reproduce. On the other hand, we are also interested to design low cost (limited memory and cpu power) camera devices, where the deployment of our deep network would be possible in practice. Both are the ultimate goals to the end users.