Difficulty-Aware Image Super Resolution via Deep Adaptive Dual-Network

Recently, deep learning based single image super-resolution(SR) approaches have achieved great development. The state-of-the-art SR methods usually adopt a feed-forward pipeline to establish a non-linear mapping between low-res(LR) and high-res(HR) images. However, due to treating all image regions equally without considering the difficulty diversity, these approaches meet an upper bound for optimization. To address this issue, we propose a novel SR approach that discriminately processes each image region within an image by its difficulty. Specifically, we propose a dual-way SR network that one way is trained to focus on easy image regions and another is trained to handle hard image regions. To identify whether a region is easy or hard, we propose a novel image difficulty recognition network based on PSNR prior. Our SR approach that uses the region mask to adaptively enforce the dual-way SR network yields superior results. Extensive experiments on several standard benchmarks (e.g., Set5, Set14, BSD100, and Urban100) show that our approach achieves state-of-the-art performance.

1. Introduction

Single image super-resolution(SISR) [1], has gained great research attention for decades, because it has been used in various computer vision applications, such as face hallucination [2], object detection [3], video compression [4], etc. As a typical ill-posed issue, Single Image Super-Resolution(SISR) aims to generate a visually clear high-resolution image I_SR from its corresponding single low-resolution image I_LR.