Image Super-Resolution via Attention based Back Projection Networks

TL;DR

This paper introduces an efficient image super-resolution network that combines attention mechanisms with back projection to improve quality while reducing computational complexity.

Contribution

It proposes an Attention based Back Projection Network (ABPN) integrating spatial attention and refined back projection blocks for enhanced super-resolution performance.

Findings

Achieves state-of-the-art results on public datasets.

Outperforms existing methods in both quality and efficiency.

Validates effectiveness through extensive experiments.

Abstract

Deep learning based image Super-Resolution (SR) has shown rapid development due to its ability of big data digestion. Generally, deeper and wider networks can extract richer feature maps and generate SR images with remarkable quality. However, the more complex network we have, the more time consumption is required for practical applications. It is important to have a simplified network for efficient image SR. In this paper, we propose an Attention based Back Projection Network (ABPN) for image super-resolution. Similar to some recent works, we believe that the back projection mechanism can be further developed for SR. Enhanced back projection blocks are suggested to iteratively update low- and high-resolution feature residues. Inspired by recent studies on attention models, we propose a Spatial Attention Block (SAB) to learn the cross-correlation across features at different layers. Based on the assumption that a good SR image should be close to the original LR image after down-sampling. We propose a Refined Back Projection Block (RBPB) for final reconstruction. Extensive experiments on some public and AIM2019 Image Super-Resolution Challenge datasets show that the proposed ABPN can provide state-of-the-art or even better performance in both quantitative and qualitative measurements.