Bi-Exponential Edge-Preserving Smoother

Thévenaz, P.; Sage, Daniel; Unser, Michaël

doi:10.1109/tip.2012.2200903

Cited by 39 publications

(18 citation statements)

References 27 publications

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“…A recursive version of the bilateral filter and a gradient domain bilateral filter were proposed in [15]. A bi-exponential recursive approach for image smoothing was proposed in [16]. A recursive anisotropic diffusion denoising algorithm was proposed in [17].…”

Section: This Paper Is a Revised And Expanded Version Of The Paper Prmentioning

confidence: 99%

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Recursive noise reduction for digital radiography images

Ostojić¹,

Starčević²,

Petrović³

2018

Telfor J

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Abstract-Modern digital radiography images are large, suggesting that a denoising algorithm should be computationally efficient. In this paper, we propose a homomorphic recursive noise reduction approach. The proposed method was compared to anisotropic diffusion on a database consisting of 47 clinical radiography images, and was subjectively deemed better at noise reduction. The proposed approach was shown to require 50 % fewer iterations to produce subjectively the same results. Objective comparison of the algorithms through a structural similarity index and peak signal-to-noise ratio showed that the proposed method outperforms anisotropic diffusion for various signal-to-noise ratios.Index Terms-digital radiography, homomorphic filtering, image noise reduction, recursive filtering.

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Section: This Paper Is a Revised And Expanded Version Of The Paper Prmentioning

confidence: 99%

“…Bias is used in the detector calibration process to ensure that 0 if there is no radiation. Under the assumption that detector is properly calibrated, can be omitted from (16). Inserting (14) into (16) gives:…”

Section: Adaptation Of Rad For Radiography Imagesmentioning

confidence: 99%

Recursive noise reduction for digital radiography images

Ostojić¹,

Starčević²,

Petrović³

2018

Telfor J

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“…Our work is directly inspired by the novel image filter BEEPS [11], which has formal links to the traditional bilateral filter, but at a much lower computational cost. The cost per pixel is constant.…”

Section: Proposed Stereo Matching Algorithmmentioning

confidence: 99%

“…As mentioned earlier, our work is directly inspired by novel BEEPS [11], which is 1D image filter. Following we will describe the process of cost volume filtering using BEEPS.…”

Section: B Cost Volume Aggregation With Beepsmentioning

confidence: 99%

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Bi-exponential Edge-Preserving Smoother Based Cost Aggregation for Stereo Matching

Zhang

2014

2014 Seventh International Symposium on Computational Intelligence and Design

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Stereo matching is one of the most important steps in computer vision systems. Broadly methods of stereo matching can be categorized into 2 types: the local support weight algorithms and global support weight algorithms. Recently adaptive local support weight algorithms have achieved state-of-art performance. However, they are still far from perfect. One of major problems of these local support weight algorithms is that they are computational complex and this complexity increases as the window size increases. In this paper we present a novel stereo matching algorithm based on Bi-Exponential Edge-Preserving Smoother (BEEPS) to make the computation efficient. The computation cost of proposed algorithm is independent of input data, filter parameters, and the degrees of smoothing. Experiments show that our algorithm greatly boost efficiency while preserve similar precision compared to state-of-art methods.

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