An Enhance Relative Total Variation With BF Model for Edge-Preserving Image Smoothing
An Enhance Relative Total Variation With BF Model for Edge-Preserving Image Smoothing
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DOI：
10.1109/tcsvt.2023.3255208
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发表时间：
2023-10
影响因子：
8.4
通讯作者：
Jun Li;Yuxuan Han;Yin Gao;Qiming Li;Sumei Wang
中科院分区：
文献类型：
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作者：
Jun Li;Yuxuan Han;Yin Gao;Qiming Li;Sumei Wang
In image processing, edge-preserving image smoothing methods that maintain weak structure while smoothing multiscale textures with strong gradients remain challenging. In this paper, a new global optimization method named Enhance Relative Total Variation Embedded with Bilateral Filtering is proposed. The texture and structure are intuitively considered separately from the model generalization. First, the weak structures are separated from the over-penalized texture and structure items by embedding bilateral filtering. Then, the in-set shrinking edges/structures are gradually recovered by constructing a contrast stretching function. In comparison to current state-of-the-art methods, experimental results demonstrate that the method is effective in maintaining weak structures and suppressing multiscale textures.
