Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network

TL;DR

This paper introduces a lightweight, efficient deep learning model for single-image super-resolution that maintains high accuracy with fewer parameters and computations, making it suitable for real-world applications.

Contribution

The paper proposes a cascading residual network architecture and variants that significantly reduce computational requirements while achieving performance comparable to state-of-the-art methods.

Findings

Models achieve high super-resolution quality with fewer parameters.

The cascading residual architecture improves efficiency without sacrificing accuracy.

Variants further enhance computational efficiency.

Abstract

In recent years, deep learning methods have been successfully applied to single-image super-resolution tasks. Despite their great performances, deep learning methods cannot be easily applied to real-world applications due to the requirement of heavy computation. In this paper, we address this issue by proposing an accurate and lightweight deep network for image super-resolution. In detail, we design an architecture that implements a cascading mechanism upon a residual network. We also present variant models of the proposed cascading residual network to further improve efficiency. Our extensive experiments show that even with much fewer parameters and operations, our models achieve performance comparable to that of state-of-the-art methods.