2023
DOI: 10.1007/s11042-023-15194-3
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Fixing algorithm of Kinect depth image based on non-local means

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Cited by 1 publication
(5 citation statements)
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“…Zhang et al [14] introduced the statistical nearest neighbors distance measurement (SNNDM) into the NLM filter (NLM-SNN) to measure the distance between two patches, thereby enhancing perceived image quality. Lin et al [15] introduced a fixing algorithm for the Kinect depth image based on nonlocal means (NLM), utilizing weights calculated on the corresponding grayscale image through a distance factor and a value-consistency factor to fill holes in the depth image. These methodologies collectively leverage diverse strategies in hole repair techniques to effectively address challenges in in-depth image repair.…”
Section: Introductionmentioning
confidence: 99%
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“…Zhang et al [14] introduced the statistical nearest neighbors distance measurement (SNNDM) into the NLM filter (NLM-SNN) to measure the distance between two patches, thereby enhancing perceived image quality. Lin et al [15] introduced a fixing algorithm for the Kinect depth image based on nonlocal means (NLM), utilizing weights calculated on the corresponding grayscale image through a distance factor and a value-consistency factor to fill holes in the depth image. These methodologies collectively leverage diverse strategies in hole repair techniques to effectively address challenges in in-depth image repair.…”
Section: Introductionmentioning
confidence: 99%
“…Additionally, the method in [14] combines the statistical nearest neighbor distance measurement so that it is more complex. In contrast, the approach proposed by [15], inspired by [14], integrates distance factors to enhance restoration efficiency for Kinect cameras but encounters challenges in intricate parameter adjustments. Drawing inspiration from [15], this paper exploits the structural similarity between grayscale and depth images.…”
Section: Introductionmentioning
confidence: 99%
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