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.
@article{arxiv.1812.05329,
title = {Wider Channel Attention Network for Remote Sensing Image Super-resolution},
author = {Jun Gu and Guangluan Xu and Yue Zhang and Xian Sun and Ran Wen and Lei Wang},
journal= {arXiv preprint arXiv:1812.05329},
year = {2019}
}
Comments
This work is proposed for remote sensing images, but the idea of the whole paper do not foucs on the characteristics of remote sensing images. The content of the article does not match the title. In this case, we want to do some experiments on the natural images to verify the three tricks in our work