Aug 2026· Remote Sensing· 0 citations· 17 references
TL;DR
A trainable dual-domain illumination-prior module that is jointly optimized with each host backbone and exploits frequency-domain and spatial-domain illumination statistics, which improves PSNR and SSIM over their corresponding baselines.
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.
SF-GAL, a prior-calibrated spatial-frequency Retinex decomposition framework for unsupervised low-light image enhancement, which calibrates a CLAHE-derived structural prior, decomposes low-light features through complementary spatial and wavelet branches, and uses structure-guided frequency modulation to regulate frequ...
Xin-Hua Dong, Yu Gao, Hongmu Han et al.· The Visual Computer· 0 citations
DPSF-Net is proposed, a dual-prior spatial-frequency network built on MCAF-Net for real-world RSID that achieves state-of-the-art performance on the real-world RRSHID remote sensing image dehazing benchmark and remains competitive across multiple synthetic datasets.
Mei Lu, Shang-Liang Shao, Shan-Liang Yao· 0 citations
Remote sensing object detection suffers from severe performance degradation under cross-domain transfer, where domain gaps arise from differences in spectral response, spatial resolution, and viewing geometry. Most existing unsupervised domain-adaptive object detection (DAOD) methods pursue cross-domain invariance thro...
Tingting Qiao, He Chen, Jue Wang et al.· IEEE Transactions on Geoscie...· 0 citations
A Relative Illumination Structure Estimation (RISE) framework is proposed that decouples relative illumination structure from absolute exposure and infers it from reliable bright regions, enabling interpretable and robust enhancement.
Tian-Le Du, Peiyuan He, Hainuo Wang et al.· 0 citations
Haze degrades image clarity, contrast, and fine details, causing perceptual ambiguity. Image dehazing aims to restore visibility, but remains challenging in noisy and complex environments. To address these challenges, prior-based dehazing techniques have been widely explored for effective transmission estimation and sc...
Suresh Babu Lam, K. Rajesh, T. S. Kumar· Journal of Visual Communicat...· 0 citations
The proposed AASFNet achieves competitive or leading performance across multiple key metrics under the evaluated experimental settings, yielding the best or second-best PSNR, SSIM, and NIQE values among the compared methods.
Yang Li, Xian-Guo Li, Dan He et al.· Electronics· 0 citations
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