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 …

Video Shadow Detection (VSD) aims to detect the shadow masks with frame sequence.
Existing works suffer from inefficient temporal learning. Moreover, few works address the …

Shadows are formed when light encounters obstacles, leading to areas of diminished
illumination. In computer vision, shadow detection, removal, and generation are crucial for …

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 …

Existing shadow removal methods face challenges when confronted with real-world scenes
particularly when dealing with complex background information and high-resolution images …

Existing unsupervised methods have addressed the challenges of inconsistent paired data
and tedious acquisition of ground-truth labels in shadow removal tasks. However, GAN …

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 …

Recent advancements in deep learning have yielded promising results for the image
shadow removal task. However, most existing methods rely on binary pre-generated …

Shadow removal is an essential task for remote sensing imagery analysis, which is tricky
due to spatial irregular and inhomogeneous degradation distribution. Unfortunately, current …

Shadow removal is crucial for enhancing image quality and facilitating downstream
computer vision tasks. However, acquiring paired shadow datasets is costly and …