WISE-Net: Wavelet-Guided Illumination Self-Supervised Enhancement for Low-Light Remote Sensing Images

Low-light remote sensing images acquired by a TDI-ICMOS system under photon-limited conditions usually exhibit severe underexposure, amplified random noise, unstable local contrast, and unnatural highlight transitions. These challenges are more difficult in our target setting because the data are real 16-bit grayscale remote sensing images without paired clean references. We propose WISE-Net, a Wavelet-guided Illumination Self-supervised Enhancement framework for low-light remote sensing images. WISE-Net follows a two-stage fully self-supervised design. In Stage 1, the input image is decomposed by Haar wavelets, and only the low-frequency component is modulated by a learned brightness-adaptive gain while the high-frequency sub-bands are preserved for structure-consistent reconstruction. A composite self-supervised objective stabilizes enhancement, protects bright regions, and reduces gain artifacts. In Stage 2, the Stage-1 enhancement network is frozen and a self-supervised blind-spot refinement network suppresses enhancement-amplified noise and local artifacts. Experiments on three real low-light remote sensing test subsets show that WISE-Net achieves the lowest NIQE on all three subsets, the lowest PIQE on test1_2048×1440 and test2_4096×1440, and the highest entropy on all three subsets.