Edge-Preserving Texture Suppression Filter Based on Joint Filtering Schemes

Su, Zhuo; Luo, Xiangfeng; Deng, Zhaohui; Liang, Yun; Ji, Zhen

doi:10.1109/tmm.2012.2237025

Cited by 56 publications

(22 citation statements)

References 38 publications

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“…Therefore, the RCR method [25] is used to adjust the object edge of the enhanced depth map. Thus, there exist several algorithms can effectively detect edges and eliminate jagged edges [26], such as guided filter [27,28], geodesic filters [29,30], weighted median filters [31,32], and bilateral filter [33][34][35]. In this paper, we suggest the rotating counsel refinement (RCR), the filtering, is used to remove the tiny jagged edge of enhanced depth maps.…”

Section: Rotating Counsel Refinement For Depth Mapmentioning

confidence: 99%

Robust depth enhancement and optimization based on advanced multilateral filters

Chang

Chou

Yang

2017

EURASIP J. Adv. Signal Process.

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Stereo matching of two distanced cameras and structured-light RGB-D cameras are the two common ways to capture the depth map, which conveys the per-pixel depth information of the image. However, the results with mismatched and occluded pixels would not provide accurately well-matched depth and image information. The mismatched depth-image relations would degrade the performances of view syntheses seriously in modern-day three-dimension video applications. Therefore, how to effectively utilize the image and depth to enhance themselves becomes more and more important. In this paper, we propose an advanced multilateral filter (AMF), which refers spatial, range, depth, and credibility information to achieve their enhancements. The AMF enhancements could sharpen the image, suppress noisy depth, filling depth holes, and sharpen the depth edges simultaneously. Experimental results demonstrate that the proposed method provides a superior performance, especially around the object boundary.

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Section: Rotating Counsel Refinement For Depth Mapmentioning

confidence: 99%

Robust depth enhancement and optimization based on advanced multilateral filters

Chang

Chou

Yang

2017

EURASIP J. Adv. Signal Process.

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“…Later, many tone mapping operators were based on base-detail decomposition using different kinds of algorithm or optimal strategies. Image decomposition with both properties of edge-preserving and texture-smoothing is important in image applications [6,7]. Durand and Dorsey [2] used bilateral-based filtering to achieve two-scale image decomposition.…”

Section: Related Workmentioning

confidence: 99%

Edge-guided filtering scheme for decomposition-based tone mapping

Luo

2014

SPIE Proceedings

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“…These approaches can be roughly classified into two categories, i.e. spatial filter [2,3,4,5] and variational model [1,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

“…It averages the nearby pixels by calculating weights from spatial and range domain, and smoothes lowcontrast regions while preserving high-contrast edges. Recognizing that BF works poorly to smooth out highcontrast textures, Su et al developed an edge-preserving texture suppression filter based on joint BF filtering scheme [4]. Inspired by the fact that extending the concept of neighborhood in a non-local way to potentially include more pixels such that may in favor of smoothing, the Regcovsmooth developed by Karacan et al used the second order statistic descriptor region covariance as a similar weight to average the pixels in a squared neighborhood [5].…”

Section: Introductionmentioning

confidence: 99%

Log-transform Weighted Total Variation for Image Smoothing

Liu¹,

Zhu²,

Wu³

2015

IJCA

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Since the input image in computer vision and graphics containing various texture/structure patterns provides rich visual information, how to properly decompose them is a challenging problem. Recent developments in high-contrast detail smoothing show that how they define edges and how this prior information guides smoothing are two key points. In this paper, we present a novel Log-transform weighted total variation (LWTV) method, which employs the signed gradient summation of Log-transform pixels at neighbor window as data-fidelity weight. Specifically, LWTV substantially improves the decomposition for the regions with faint pixelboundary and alleviates the drawback of slightly blurry. Experimental results demonstrate that the proposed method has appearance performance on image with abundant uniform textural details.

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Edge-Preserving Texture Suppression Filter Based on Joint Filtering Schemes

Cited by 56 publications

References 38 publications

Robust depth enhancement and optimization based on advanced multilateral filters

Robust depth enhancement and optimization based on advanced multilateral filters

Edge-guided filtering scheme for decomposition-based tone mapping

Log-transform Weighted Total Variation for Image Smoothing

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