Edge-enhanced efficient network for remote sensing image super-resolution
Tianlin Zhang, Hongzhen Chen, Shi Chen, Chunjiang Bian · International Journal of Remote Sensing · 2022
The super-resolution (SR) reconstruction is drawing increasing attention in remote-sensing image processing, owing to improving the spatial resolution and enriching the details of initially obtaining low-resolution (LR) images. Different from natural images, remote sensing images usually have more complex image distribution and degradation processes. Although current deep convolutional neural network (DCNN)-based approaches reach better performance by deepening the network and introducing attention mechanism, this is usually accompanied by more parameters, more computation, and more complex designs. To improve the performance and efficiency of SR reconstruction of remote sensing images, we proposed the edge-enhanced efficient network (EESR). Specifically, we design an inception-like multi-branch convolution block, named edge-enhanced convolution block (EEB), to enrich edge-aware capabilities of the network, which includes multi-order gradient extraction and other feature enhancement branches. Furthermore, re-parameterization is introduced into the inference stage of the network, aiming at boosting efficient inference. We re-parameterize the weights of EEB to the edge-enhanced convolution (EEC) via equivalent transformation to reduce the parameters and computational cost of the inference model. In addition, we construct a paired SR dataset named GF2-Port for remote sensing images with degradation simulation, based on the data of GaoFen-2 port and strait. Extensive experiments on both established and public datasets indicate that the proposed EESR outperforms other comparable approaches in terms of balancing restoration accuracy and inference efficiency.