Improving Super Resolution Methods Via Incremental Residual Learning

Recently, Convolutional Neural Networks (CNNs) have shown promising performance in super-resolution (SR). However, these methods operate primarily on Low Resolution (LR) inputs for memory efficiency but this limits, as we demonstrate, their ability to (i) model high frequency information; and (ii) smoothly translate from LR to High Resolution (HR) space. To this end, we propose a novel Incremental Residual Learning (IRL) framework to address these mentioned issues. In IRL, first we select a typical SR pre-trained network as a master branch. Next we sequentially train and add residual branches to the main branch, where each residual branch is learned to model accumulated residuals of all previous branches. We plug state of the art methods in IRL framework and demonstrate consistent performance improvement on public benchmark datasets to set a new state of the art for SR at only ≈ 20% increase in training time.

1. INTRODUCTION

Single Image Super-Resolution (SR) aims to generate a High Resolution (HR) image I^SR from a low resolution (LR) image I^LR such that it is similar to original HR image I^HR. SR has seen a lot of interest recently because it is: (i) inherently an ill-posed inverse problem; and (ii) an important low level vision problem having many applications.