Hsieh, Shih WeiShih WeiHsiehYang, Chih HsiangChih HsiangYangYI-CHANG LU2023-06-192023-06-192022-01-01978166548563019457871https://scholars.lib.ntu.edu.tw/handle/123456789/632838In this paper, we develop a novel shadow removal technique where the inputs are a single natural image to be restored and its corresponding shadow mask. We first decompose the image by super-pixels and cluster them into several sim-ilar regions. Then we train a random forest model to pre-dict matched pairs between shadow and non-shadow regions. By applying a distribution-based mapping function on the matched pairs, we can relight pixels in those shadow regions. An optimization framework based on half-quadratic splitting (HQS) method is also introduced to further improve the qual-ity of the mapping process. We also design a post-processing stage with a boundary inpainting function to generate bet-ter visual results. Our experiments show that the proposed method can remove shadows effectively and produce high-quality shadow-free images.color adjustment | HQS optimization | region matching | Shadow removal[SDGs]SDG15conference paper10.1109/ICME52920.2022.98589332-s2.0-85137692842https://api.elsevier.com/content/abstract/scopus_id/85137692842