  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.
