Shadow Removal Through Learning-Based Region Matching and Mapping Function Optimization

In 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 matche...

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Bibliographic Details
Published in2022 IEEE International Conference on Multimedia and Expo (ICME) pp. 1 - 6
Main Authors Hsieh, Shih-Wei, Yang, Chih-Hsiang, Lu, Yi-Chang
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.07.2022
Subjects
color adjustment
HQS optimization
Image restoration
Optimization
Predictive models
Random forests
region matching
Shadow removal
Visualization
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Summary:In 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.
ISSN:1945-788X
DOI:10.1109/ICME52920.2022.9858933