A Generalized Framework for Edge-Preserving and Structure-Preserving Image Smoothing
نویسندگان
چکیده
Image smoothing is a fundamental procedure in applications of both computer vision and graphics. The required properties can be different or even contradictive among tasks. Nevertheless, the inherent nature one operator usually fixed thus cannot meet various requirements applications. In this paper, we first introduce truncated Huber penalty function which shows strong flexibility under parameter settings. A generalized framework then proposed with introduced function. When combined its flexibility, our able to achieve diverse natures where behaviors achieved. It also yield behavior that seldom achieved by previous methods, superior performance challenging cases. These together enable capable range outperform state-of-the-art approaches several tasks, such as image detail enhancement, clip-art compression artifacts removal, guided depth map restoration, texture etc. addition, an efficient numerical solution provided convergence theoretically guaranteed optimization non-convex non-smooth. simple yet effective approach further reduce computational cost method while maintaining performance. effectiveness are validated through comprehensive experiments Our code available at https://github.com/wliusjtu/Generalized-Smoothing-Framework .
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ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2022
ISSN: ['1939-3539', '2160-9292', '0162-8828']
DOI: https://doi.org/10.1109/tpami.2021.3097891