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- W2901148082 abstract "In this paper, a local activity measurement of the clipped and normalized variance or standard deviation is proposed to drive anisotropic diffusion and relative total variation (RTV) to work better for structural preservation. Firstly, two novel edge-stop functions are introduced for our local activity-driven anisotropic diffusion (LAD-AD) to efficiently remove severe artifacts and preserve the fine geometry structures in HEVC-compressed depth images. Secondly, we propose a simple yet effective local activity-driven RTV (LAD-RTV) with the way of the product between gradient and the local activity measurement for image smoothing and scale representation. Meanwhile, both color-sharing information and each-channel discriminative information are considered, which are significant to color image edge-preserving but not included in the RTV model. Besides, LAD-RTV leverages the form of the division of gradient and the local activity measurement to resolve the problem of general image de-noising by regarding the noises as the duplicate texture elements. Experimental results have validated that the proposed LAD-AD can greatly improve the precision of the HEVC-compressed depth image and the quality of its synthesized image. Additionally, large numbers of results have shown our LAD-RTV is superior to several existing methods." @default.
- W2901148082 created "2018-11-29" @default.
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- W2901148082 date "2019-04-01" @default.
- W2901148082 modified "2023-09-24" @default.
- W2901148082 title "Local activity-driven structural-preserving filtering for noise removal and image smoothing" @default.
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- W2901148082 doi "https://doi.org/10.1016/j.sigpro.2018.11.012" @default.
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