Fast bilateral filter for edge-preserving smoothing

Guarnieri, Gabriele; Marsi, Stefano; Ramponi, Giovanni

doi:10.1049/el:20064369

Cited by 24 publications

(12 citation statements)

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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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“…By convention, it is customary to assume that the values taken by the range filter r satisfy r ∈ [0, 1], so that a small r forbids any sort of smoothing, and a large r authorizes the smoothing provided by the spatial filter s. By design, the denominator of (17) ensures a proper normalizationobserve that, through r , this normalization depends on data in a nonlinear fashion and must therefore be computed anew at every coordinate k. For the purpose of discussion, we assume 1-D data, with d = 1. Moreover, we take s to be the bi-exponential filter (11). This leads us to rewrite (17) as…”

Section: B Bilateral Filtermentioning

confidence: 99%

“…This method was used for instance in [10] to homogenize the illumination of an image by compressing its dynamic range, and further refined in [11]- [13]. Substantial acceleration was also achieved in [14], again at the cost of some form of quantization.…”

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confidence: 99%

Bi-Exponential Edge-Preserving Smoother

Thévenaz

Sage

Unser

2012

IEEE Trans. on Image Process.

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Abstract-Edge-preserving smoothers need not be taxed by a severe computational cost. We present, in this paper, a lean algorithm that is inspired by the bi-exponential filter and preserves its structure-a pair of one-tap recursions. By a careful but simple local adaptation of the filter weights to the data, we are able to design an edge-preserving smoother that has a very low memory and computational footprint while requiring a trivial coding effort. We demonstrate that our filter (a bi-exponential edge-preserving smoother, or BEEPS) has formal links with the traditional bilateral filter. On a practical side, we observe that the BEEPS also produces images that are similar to those that would result from the bilateral filter, but at a much-reduced computational cost. The cost per pixel is constant and depends neither on the data nor on the filter parameters, not even on the degree of smoothing.Index Terms-Bi-exponential filter, bilateral filter, nonlocal means, recursive filter.

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“…͑8͒ becomes a convolution that can be efficiently computed using the fast Fourier transform ͑FFT͒ algorithm. In 2006, Guarnieri et al 17 improved the PWL approximation of the bilateral filter introducing the following modifications:…”

Section: Bf-based Deghostingmentioning

confidence: 99%

Bilateral filter-based adaptive nonuniformity correction for infrared focal-plane array systems

2010

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In scene-based nonuniformity correction (NUC) methods for infrared focal-plane array cameras, the problem of ghosting artifacts widely affects the sensitivity of the imaging system and visibly decreases the image quality. Ghosting artifacts can also degrade the performance of several applications, such as target detection and tracking. We carried out a detailed analysis of the problem using a well-established NUC technique: the least mean square Scribner's algorithm. In order to solve some drawbacks of the original Scribner's algorithm, we introduced in the scheme a new technique that mitigates ghosting. Such technique relies on the employment of an edge-preserving spatial filter for the purpose of computing reliable spatial estimates. We tested the effectiveness of the new technique applying the improved NUC method to an experimental IR sequence of frames acquired in the laboratory. Finally, the performance of the proposed method was discussed and compared to that yielded by a well-established deghosting technique. c 2010 Society of Photo-Optical Instrumentation Engineers. [DOI:10.1117/1.3425660

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Fast bilateral filter for edge-preserving smoothing

Cited by 24 publications

References 1 publication

Robust depth enhancement and optimization based on advanced multilateral filters

Robust depth enhancement and optimization based on advanced multilateral filters

Bi-Exponential Edge-Preserving Smoother

Bilateral filter-based adaptive nonuniformity correction for infrared focal-plane array systems

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