Towards non-uniform shadow removal using shadow adaptive normalization

Abstract

Image shadow removal has received increasing attention in recent years. Existing deep learning-based shadow removal methods usually rely on the assumption of globally uniform illumination or employ simplified multiplicative illumination models to estimate parameters related to shadow-free image reconstruction. However, the shading process often contains non-uniform, diverse, and complex shadow patterns, substantially reducing the robustness of current shadow removal techniques and limiting their performance. To address this issue, this paper revisits the traditional shadow degradation model and introduces a pixel-wise adaptive non-uniform illumination model. Building upon this model, a shadow adaptive normalization (SAN) module is designed to estimate the parameter maps of the illumination model and rectify the shadow features within the latent space. The proposed SAN dynamically performs attentive normalization on shadow region features, which can align the statistical distributions of shadow and non-shadow regions. To enhance the efficiency of the SAN module, this paper introduces an intra-module complexity reduction strategy to reduce computational complexity while improving the stability of the training process. In addition, to mitigate the color deviation between paired training data, this paper introduces a Poisson-function-based loss to achieve color robustness. Extensive experiments on image shadow triplet dataset (ISTD), adjusted image shadow triplet dataset (ISTD+), and shadow removal dataset (SRD) validate the superiority of the proposed method over other state-of-the-art (SOTA) approaches.