# LSR: A Light-Weight Super-Resolution Method

## TL;DR

This paper introduces LSR, a lightweight super-resolution method designed for mobile devices, which predicts residual images using a self-supervised framework without heavy deep networks, achieving good quality with low complexity.

## Contribution

The paper presents a novel lightweight super-resolution approach that combines unsupervised and supervised learning modules, avoiding end-to-end deep networks for efficiency.

## Key findings

- LSR achieves higher PSNR/SSIM than classical methods.
- It has low computational complexity suitable for mobile platforms.
- Offers better visual quality compared to exemplar-based methods.

## Abstract

A light-weight super-resolution (LSR) method from a single image targeting mobile applications is proposed in this work. LSR predicts the residual image between the interpolated low-resolution (ILR) and high-resolution (HR) images using a self-supervised framework. To lower the computational complexity, LSR does not adopt the end-to-end optimization deep networks. It consists of three modules: 1) generation of a pool of rich and diversified representations in the neighborhood of a target pixel via unsupervised learning, 2) selecting a subset from the representation pool that is most relevant to the underlying super-resolution task automatically via supervised learning, 3) predicting the residual of the target pixel via regression. LSR has low computational complexity and reasonable model size so that it can be implemented on mobile/edge platforms conveniently. Besides, it offers better visual quality than classical exemplar-based methods in terms of PSNR/SSIM measures.