文献类型：期刊文献

英文题名：Enhancing unsupervised shadow removal via multi-intensity shadow generation and diffusion modeling: Enhancing unsupervised shadow removal..: D. Wang et al.

作者：Wang, Donghui[1]; Wang, Jinhua[1]; He, Ning[1]; Zhang, Jingzun[1]; Zhang, Sen[1]; Liu, Shuai[1]

第一作者：Wang, Donghui

机构：[1] Smart City College, Beijing Union University, Beijing, Chaoyang, 100101, China

第一机构：北京联合大学智慧城市学院

年份：2024

外文期刊名：Visual Computer

收录：EI(收录号：20245017526869);Scopus(收录号：2-s2.0-85211765868)

语种：英文

外文关键词：Benchmarking - Computer vision - Image enhancement - Signal to noise ratio

摘要：Shadow removal is crucial for enhancing image quality and facilitating downstream computer vision tasks. However, acquiring paired shadow datasets is costly and challenging. This paper presents an unsupervised shadow removal algorithm leveraging a diffusion model. It initially generates a shadow-free image through a brightness enhancement network, which pairs with the original shadow image to train a shadow generation network. A shadow intensity control module ensures diverse shadow intensities, addressing data scarcity. During shadow removal, a resampling module constrains effects within shadow areas, while a boundary artifact removal network eliminates artifacts. Experimental results demonstrate the method’s superiority over existing unsupervised methods, achieving state-of-the-art performance on benchmark datasets with improved PSNR (+ 1.46 dB) and reduced RMSE (-?1.4) in shadow regions. The source code and pre-trained models are available at https://github.com/Donghui-Wang/SMGDM-SRA ? The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.