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Open Access

RFCNet: Remote Sensing Image Super-Resolution Using Residual Feature Calibration Network

College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Shanghai Institute of Satellite Engineering, Shanghai 201109, China
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Abstract

In the field of single remote sensing image Super-Resolution (SR), deep Convolutional Neural Networks (CNNs) have achieved top performance. To further enhance convolutional module performance in processing remote sensing images, we construct an efficient residual feature calibration block to generate expressive features. After harvesting residual features, we first divide them into two parts along the channel dimension. One part flows to the Self-Calibrated Convolution (SCC) to be further refined, and the other part is rescaled by the proposed Two-Path Channel Attention (TPCA) mechanism. SCC corrects local features according to their expressions under the deep receptive field, so that the features can be refined without increasing the number of calculations. The proposed TPCA uses the means and variances of feature maps to obtain accurate channel attention vectors. Moreover, a region-level nonlocal operation is introduced to capture long-distance spatial contextual information by exploring pixel dependencies at the region level. Extensive experiments demonstrate that the proposed residual feature calibration network is superior to other SR methods in terms of quantitative metrics and visual quality.

Keywords

Convolutional Neural Network (CNN)remote sensing imageSuper-Resolution (SR)attention mechanism

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Tsinghua Science and Technology
Volume 28 Issue 3,
June 2023
Pages 475-485
DOI: 10.26599/TST.2022.9010018
Cite this article:
Xue Y, Li L, Wang Z, et al. RFCNet: Remote Sensing Image Super-Resolution Using Residual Feature Calibration Network. Tsinghua Science and Technology, 2023, 28(3): 475-485. https://doi.org/10.26599/TST.2022.9010018

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Received: 13 January 2022
Revised: 05 April 2022
Accepted: 24 May 2022
Published: 13 December 2022
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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