Wavelet-Based Dual Recursive Network for Image Super-Resolution

Although remarkable progress has been made on single-image super-resolution (SISR), deep learning methods cannot be easily applied to real-world applications due to the requirement of its heavy computation, especially for mobile devices. Focusing on the fewer parameters and faster inference SISR approach, we propose an efficient and time-saving wavelet transform-based network architecture, where the image super-resolution (SR) processing is carried out in the wavelet domain. Different from the existing methods that directly infer high-resolution (HR) image with the input low-resolution (LR) image, our approach first decomposes the LR image into a series of wavelet coefficients (WCs) and the network learns to predict the corresponding series of HR WCs and then reconstructs the HR image. Particularly, in order to further enhance the relationship between WCs and image deep characteristics, we propose two novel modules [wavelet feature mapping block (WFMB) and wavelet coefficients reconstruction block (WCRB)] and a dual recursive framework for joint learning strategy, thus forming a WCs prediction model to realize the efficient and accurate reconstruction of HR WCs. Experimental results show that the proposed method can outperform state-of-the-art methods with more than a $2\times $ reduction in model parameters and computational complexity.

I. Introduction

Image super-resolution (SR), particularly single-image super-resolution (SISR) shows great potential in many fields, such as medical imaging [1], satellite imaging [2], and security and surveillance [3], and it has gained increasing research attention for decades. SISR aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) image, which is a highly ill-posed problem due to the many-to-one mapping. Although it is really a challenging issue, great research efforts have been made to solve this problem.