Wider Channel Attention Network for Remote Sensing Image Super-resolution

TL;DR

This paper introduces WCAN, a novel super-resolution network for remote sensing images that uses channel attention and local memory connections to enhance information flow and improve image quality.

Contribution

The paper proposes a Wider Channel Attention Network with local memory connections, improving feature recalibration and information flow for remote sensing image super-resolution.

Findings

Achieves better accuracy than state-of-the-art methods.

Provides visual quality improvements in super-resolved images.

Demonstrates effectiveness on UC Merced dataset.

Abstract

Recently, deep convolutional neural networks (CNNs) have obtained promising results in image processing tasks including super-resolution (SR). However, most CNN-based SR methods treat low-resolution (LR) inputs and features equally across channels, rarely notice the loss of information flow caused by the activation function and fail to leverage the representation ability of CNNs. In this letter, we propose a novel single-image super-resolution (SISR) algorithm named Wider Channel Attention Network (WCAN) for remote sensing images. Firstly, the channel attention mechanism is used to adaptively recalibrate the importance of each channel at the middle of the wider attention block (WAB). Secondly, we propose the Local Memory Connection (LMC) to enhance the information flow. Finally, the features within each WAB are fused to take advantage of the network's representation capability and further improve information and gradient flow. Analytic experiments on a public remote sensing data set (UC Merced) show that our WCAN achieves better accuracy and visual improvements against most state-of-the-art methods.