Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 60, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2021.3132093
Keywords
Feature extraction; Superresolution; Convolution; Remote sensing; Task analysis; Image reconstruction; Benchmark testing; Contextual feature learning; image super-resolution; lightweight neural network; remote sensing
Categories
Funding
- National Natural Science Foundation of China [61273273]
- National Key Research and Development Plan [2017YFC0112001]
- China Central Television [JG2018-0247]
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This paper proposes a lightweight super-resolution network that reduces the network burden by replacing convolution layers while maintaining performance. The authors introduce a lightweight convolution layer called the contextual transformation layer (CTL) for remote-sensing image super-resolution. Experimental results demonstrate the effectiveness of the proposed method in remote-sensing image super-resolution, natural image super-resolution, and denoising tasks.
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 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 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.
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