Neural Implicit Fourier Transform for Remote Sensing Shadow Removal

Remote sensing shadow removal is an open issue. Previous studies focus on working in the spatial dimension, ignoring the potential of the Fourier dimension, while illumination degradation typically exists in the amplitude component. To address this limitation, our insight is a fresh dual-stage Fourier-based network (NeFour), which explores the best of both worlds between frequency and spatial information. In the frequency stage, we investigate the positive correlation between amplitude and brightness from channel and spatial statistics. Coupled with implicitly defined normalization, a controllable fitting amplitude transform map recreates the illumination. In the spatial stage, the inverted dark channel prior with 3-D coordinates serves as modulation matrices that naturally reveal the spatial distribution of shadows, thus elegantly eliminating shadow remnants. With ingenious design, NeFour achieves nontrivial performance against state-of-the-art shadow removal methods in terms of both visual perception and quantitative evaluation. The code is publicly available at https://github.com/chi-kaichen/NeFour.