Shadows often occur when we capture the document with casual equipment, which
influences the visual quality and readability of the digital copies. Different from the …

The spatial non-uniformity and diverse patterns of shadow degradation conflict with the
weight sharing manner of dominant models which may lead to an unsatisfactory …

Diffusion models have revolutionized image editing but often generate images that violate
physical laws, particularly the effects of objects on the scene, eg, occlusions, shadows, and …

The removal of shadows from images is a classic problem in computer vision, aiming to
restore the lighting in shadowed areas, thereby reducing the information interference and …

Recovering textures beneath shadows has remained a challenging problem due to the
inherent difficulty of inferring shadow-free scenes from shadow images. In this paper, we …

Shadow removal aims at restoring the image content within shadow regions, pursuing a
uniform distribution of illumination that is consistent between shadow and non-shadow …

Recent deep learning methods have achieved superior results in shadow removal.
However, most of these supervised methods rely on training over a huge amount of shadow …

In this paper we present S3R-Net the Self-Supervised Shadow Removal Network. The two-
branch WGAN model achieves self-supervision relying on the unify-and-adapt phenomenon …

Fully-supervised shadow removal methods achieve the best restoration qualities on public
datasets but still generate some shadow remnants. One of the reasons is the lack of large …

Removing shadows requires an understanding of both lighting conditions and object
textures in a scene. Existing methods typically learn pixel-level color mappings between …