Contextual Transformation Network for Lightweight Remote-Sensing Image Super-Resolution

Current super-resolution networks typically reduce network parameters and multiadds operations by designing lightweight structures, but lightening the convolution layer is often ignored. In this work, we observe that 3 3 convolutions occupy a high percentage of network parameters in most lightweight super-resolution networks. This motivates us to consider lightening super-resolution networks by replacing 3 3 convolutions with lightweight convolutions, while maintaining the performance. To achieve this, we propose a lightweight convolution layer named contextual transformation layer (CTL). It can yield efficient contextual features through a context feature extraction module and enrich extracted contextual features through a context feature transformation module. Based on CTLs, we build a lightweight super-resolution network called contextual transformation network (CTN) for remote-sensing image super-resolution. Specifically, we use two CTLs to construct a contextual transformation block (CTB) for hierarchical feature learning. Interleaved with a CTB, a context enhancement module (CEM) is employed to enhance the extracted feature representations. All extracted features are processed by a contextual feature aggregation module for final remote-sensing image super-resolution. Extensive experiments are performed on a remote-sensing image super-resolution benchmark named UC Merced. Our method achieves superior results to the other state-of-the-art methods. To demonstrate the generalization ability of our CTL, we extend our CTN to two relevant tasks: natural image super-resolution and natural image denoising. Experimental results on natural image super-resolution benchmarks (i. e. , Set5, Set14, B100, Urban100, and Manga109) and natural image denoising benchmarks (i. e. , SIDD and DND) further prove the superiority of our method. Our code is publicly available at https: //github. com/BITszwang/CTNet.