Shadow removal, which aims to restore the background in the shadow regions, is
challenging due to the highly ill-posed nature. Most existing deep learning-based methods …

In this paper, we propose a novel two-stage context-aware network named CANet for
shadow removal, in which the contextual information from non-shadow regions is transferred …

Shadow removal is a computer-vision task that aims to restore the image content in shadow
regions. While almost all recent shadow-removal methods require shadow-free images for …

We propose residual denoising diffusion models (RDDM) a novel dual diffusion process that
decouples the traditional single denoising diffusion process into residual diffusion and noise …

Shadow removal is an important topic in image restoration, and it can benefit many
computer vision tasks. State-of-the-art shadow-removal methods typically employ deep …

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 …

Abstract Deep Convolutional Neural Networks (CNNs) based methods have achieved
significant breakthroughs in the task of single image shadow removal. However, the …

Document image enhancement methods are often used to improve the accuracy and
efficiency of automated document analysis and recognition tasks such as character …

Existing works on document image shadow removal mostly depend on learning and
leveraging a constant background (the color of the paper) from the image. However, the …

In this paper, a novel histogram modification-based bi histogram equalization (HE) approach
for contrast enhancement on digital images is presented. At first, a power-logarithm …