Fast Non-Local Means (NLM) Computation With Probabilistic Early Termination

Vignesh, R.; Oh, Byung Tae; Kuo, C.-C. Jay

doi:10.1109/lsp.2009.2038956

Cited by 95 publications

(45 citation statements)

References 9 publications

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“…It is clear that HOSVD helps keep image details and weak edges, and has a more random-like method noise than LJSCPW. The denoising performance is then evaluated by computing its mean and standard deviation in terms of PSNR [8], [9] and SSIM [11].…”

Section: Resultsmentioning

confidence: 99%

Image Data Denoising using Center Pixel Weights in Non-Local Means and Smart Patch-based, Modern Machine Learning Method using Higher Order Singular Value Decomposition: A Review

Rajak¹,

Chugh²

2015

IJCA

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In this work, there is a comparison related to image denoising techniques between center pixel weights (CPW) in Non-Local Means (NLM) and smart patch-based, modern technique using the higher order singular value decomposition (HOSVD). The HOSVD technique simply compose in a cluster, alike Patches of noisy image in 3D heap, work out HOSVD factors of this heap, handles these factors by stiff thresholding, and turn upside down the HOSVD transmute to yield the final resultant image. Whereas (NLM) and its variants have proven to be effective and robust in many image denoising tasks. It is experimentally demonstrating approximately 12 percent improved PSNR characteristics of HOSVD technique on gray scale images. The HOSVD process yields state-of-the-art outcomes on gray images, than the center pixel weights (CPW) in NLM image data denoising process at moderately great noise stages.

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Section: Resultsmentioning

confidence: 99%

Image Data Denoising using Center Pixel Weights in Non-Local Means and Smart Patch-based, Modern Machine Learning Method using Higher Order Singular Value Decomposition: A Review

Rajak¹,

Chugh²

2015

IJCA

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“…The method can be extended to multilateral filter by applying Gaussian weights for f (q − p). Various approximation [22], [7], [9], [35] provide results comparable to the original implementation while further reducing the computational cost. A different related approach [6] expresses the filter as a series of convolutions for which fast and computationally efficient implementations exist.…”

Section: B Upsampling Based On Non-local Means Filtermentioning

confidence: 97%

Novel methods for image-guided ToF depth upsampling

Eichhardt

Jankó

Chetverikov

2016

2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Abstract-Sensor fusion is an important part of modern cyberphysical systems that observe and analyse real-world environments. Time-of-Flight depth cameras provide high frame rate low-resolution depth data that can be efficiently used in many applications related to cyber-physical systems. In this paper, we address the critical issue of upsampling and enhancing the low-quality depth data using a calibrated and registered highresolution colour image or video. Two novel algorithms for image-guided depth upsampling are proposed based on different principles. A new method for video-guided upsampling is also presented. Initial test results on synthetic and real data are shown and discussed.

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“…It implements Fast Non Local means filter for denoising followed by the thresholding of the method noise. In section II Non Local means filter proposed by Buades et al in [2] and Fast Non Local means as proposed in [9] are discussed followed by Wavelet thresholding in section III. Section IV of this paper deals with the proposed methodology.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Image Denoising Technique based on FNLM and Wavelet Thresholding

Shuchi¹

2015

IJIRCCE

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ABSTRACT:Image denoising constitutes the first step in image processing domain. In order to solve this frequently occurring problem various methods have been proposed. In this paper a denoising algorithm is proposed which uses the combination of Fast Non-Local means filter (FNLM) and adaptive wavelet thresholding. The performance of FNLM filter deteriorates with the increase of noise amount in the image. This hybrid approach solves this problem. Firstly, the image is denoised by the FNLM filter and then its method noise is obtained. The method noise is subjected to adaptive wavelet thresholding for extracting the image details that were removed by the FNLM filter. These extracted details are added to the output of FNLM filter and a noise free image is recovered with all the edges and image details preserved.

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Fast Non-Local Means (NLM) Computation With Probabilistic Early Termination

Cited by 95 publications

References 9 publications

Image Data Denoising using Center Pixel Weights in Non-Local Means and Smart Patch-based, Modern Machine Learning Method using Higher Order Singular Value Decomposition: A Review

Image Data Denoising using Center Pixel Weights in Non-Local Means and Smart Patch-based, Modern Machine Learning Method using Higher Order Singular Value Decomposition: A Review

Novel methods for image-guided ToF depth upsampling

A Hybrid Image Denoising Technique based on FNLM and Wavelet Thresholding

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