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Structure-preserving image smoothing with semantic cues

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Abstract

The purpose of image smoothing is to smooth out low-contrast textures while preserving meaningful structures. Although this problem has been studied for decades, it still leaves a lot of space to improve. Recently, learning-based edge detectors have superior performance to traditional manually-designed detectors. Based on the edge detection technique, we present a novel optimization-based image smoothing model combining semantic prior and perform \(L_0\) gradient minimization recursively in our framework to refine the result. Our framework combines the advantage of the state-of-the-art edge detector and the ability of \(L_0\) gradient minimization for structure-preserving image smoothing. Moreover, we employ a large number of real-world images and perform various experiments to evaluate our algorithm. Experimental results show that our algorithm outperforms state-of-the-art algorithms, especially in extracting subjectively-meaningful structures.

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Notes

  1. high-frequency textures belonging to input color image are wrongly copied to the output refined depth map, leading to visual artifacts. This is a common issue difficult to solve in the depth upsampling community.

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Authors and Affiliations

  1. Yunzhangfang Network Technology Co., Ltd., Nanjing, 210000, China

    Linggang Chen

  2. School of Computer Science, Wuhan University, Wuhan, 430072, China

    Gang Fu

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  1. Linggang Chen
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  2. Gang Fu
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Correspondence to Gang Fu.

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Chen, L., Fu, G. Structure-preserving image smoothing with semantic cues. Vis Comput 36, 2017–2027 (2020). https://doi.org/10.1007/s00371-020-01950-1

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