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Article

Dual-Domain Illumination Prior for Low-Light Remote Sensing Image Enhancement

1
Donghai Laboratory, Zhoushan 316021, China
2
School of Information Engineering, Zhejiang Ocean University, Zhoushan 316021, China
3
School of Mathematical Sciences, Anhui University, Hefei 230601, China
4
School of Mathematical Sciences, Northeast Normal University, Changchun 130024, China
5
Xinjiang Jurong Energy (Group) Co., Ltd., Urumqi 841603, China
6
College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2026, 18(16), 2817; https://doi.org/10.3390/rs18162817
Submission received: 30 June 2026 / Revised: 2 August 2026 / Accepted: 18 August 2026 / Published: 20 August 2026

Abstract

Low-light conditions degrade remote sensing imagery by reducing contrast, distorting color, and obscuring fine terrain structures and small objects critical for Earth observation. Accurate illumination adjustment under spatially varying scene content remains challenging for existing enhancement methods, and many prior-guided approaches operate exclusively in either the spatial domain or the frequency domain. In this work, we propose a Dual-Domain Illumination Prior (DDIP), a trainable dual-domain illumination-prior module that is jointly optimized with each host backbone and exploits frequency-domain and spatial-domain illumination statistics. DDIP comprises three components: a Frequency-Domain Illumination Distribution Prior (FIDP) that performs per-color-channel amplitude calibration in Fourier space to improve global brightness; a Spatial-Domain Illumination Distribution Prior (SIDP), adapted from IDP-Net, that performs multi-scale sub-region statistical correction for local illumination adjustment; and a Selective Core Feature Fusion (SCFF) module that adaptively combines the frequency-domain output, the spatial-domain output, and the original input through an attention-based gating mechanism with dual pooling. DDIP is integrated with each host backbone while leaving its main restoration blocks unchanged. In the controlled reconstruction comparisons on iSAID-dark and the evaluated general low-light benchmarks, equipping the tested backbone networks with DDIP improves PSNR and SSIM over their corresponding baselines. Complementary LPIPS and CIELAB lightness measurements characterize perceptual similarity and lightness behavior, while a fixed-detector object-detection evaluation on the tested high-resolution iSAID-dark scenes examines the effect of the enhancement pipelines under the reported synthetic low-light conditions. The ablation studies further examine the contribution of the module components within the reported experimental settings.
Keywords: low-light image enhancement; remote sensing imagery; dual-domain illumination prior; Fourier transform; selective core feature fusion low-light image enhancement; remote sensing imagery; dual-domain illumination prior; Fourier transform; selective core feature fusion

Share and Cite

MDPI and ACS Style

Wang, C.; Pan, Z.; He, L.; Liu, J.; Mei, L.; Lin, R.; Chen, H.; Yang, C. Dual-Domain Illumination Prior for Low-Light Remote Sensing Image Enhancement. Remote Sens. 2026, 18, 2817. https://doi.org/10.3390/rs18162817

AMA Style

Wang C, Pan Z, He L, Liu J, Mei L, Lin R, Chen H, Yang C. Dual-Domain Illumination Prior for Low-Light Remote Sensing Image Enhancement. Remote Sensing. 2026; 18(16):2817. https://doi.org/10.3390/rs18162817

Chicago/Turabian Style

Wang, Chao, Zhe Pan, Liangtian He, Jun Liu, Lin Mei, Rongsheng Lin, Hongming Chen, and Chuansheng Yang. 2026. "Dual-Domain Illumination Prior for Low-Light Remote Sensing Image Enhancement" Remote Sensing 18, no. 16: 2817. https://doi.org/10.3390/rs18162817

APA Style

Wang, C., Pan, Z., He, L., Liu, J., Mei, L., Lin, R., Chen, H., & Yang, C. (2026). Dual-Domain Illumination Prior for Low-Light Remote Sensing Image Enhancement. Remote Sensing, 18(16), 2817. https://doi.org/10.3390/rs18162817

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