WDN: A Wide and Deep Network to Divide-and-Conquer Image Super-resolution

TL;DR

This paper introduces a novel wide and deep neural network architecture that employs a divide-and-conquer strategy to improve image super-resolution, achieving sharper and better results than existing methods.

Contribution

It proposes a new wide and deep neural network architecture that explicitly incorporates divide-and-conquer principles for image super-resolution.

Findings

Outperforms state-of-the-art methods on five datasets.

Produces sharper and higher-quality super-resolved images.

Introduces a feature map intensity calibration technique.

Abstract

Divide and conquer is an established algorithm design paradigm that has proven itself to solve a variety of problems efficiently. However, it is yet to be fully explored in solving problems with a neural network, particularly the problem of image super-resolution. In this work, we propose an approach to divide the problem of image super-resolution into multiple sub-problems and then solve/conquer them with the help of a neural network. Unlike a typical deep neural network, we design an alternate network architecture that is much wider (along with being deeper) than existing networks and is specially designed to implement the divide-and-conquer design paradigm with a neural network. Additionally, a technique to calibrate the intensities of feature map pixels is being introduced. Extensive experimentation on five datasets reveals that our approach towards the problem and the proposed architecture generate better and sharper results than current state-of-the-art methods.