REMOTE SENSING IMAGE SUPER-RESOLUTION VIA ENHANCED BACK-PROJECTION NETWORKS

Convolutional neural network (CNN)-based image super-resolution (SR) is one of the most active field of research in the remote sensing community. As a state-of-the-art super-resolving method, however, the dense deep back-projection network (DDBPN) ignores the mutual differences among the channel-wise features and discards the initial feature when performing reconstruction. In this paper, we develop an enhanced back-projection network (EBPN) with performance exceeding the DDBPN and other state-of-the-art methods. The performance improvement gains from introducing attention mechanism to capture the feature differences among channels and reconstructing images by using the element-wise sum of the upscaled initial feature and deep features learned at different depths. A retraining strategy is also employed to further boost the SR ability of EBPN for remote sensing images. Experimental results on a remote sensing dataset and four benchmark datasets demonstrate the superiority of EBPN.