Probabilistic Non-Local Means

Wu, Yue; Tracey, Brian H.; Natarajan, Prem; Noonan, Joseph P.

doi:10.1109/lsp.2013.2263135

Cited by 78 publications

(57 citation statements)

References 17 publications

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“…Recent studies in additive Gaussian noise reduction have shown that similarities between overlapping patches should be weighted using a dedicated kernel [52]. This idea could be extended to our SAR denoising method by adapting the kernel ψ.…”

Section: Discussionmentioning

confidence: 99%

NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising

Deledalle

Dénis

Tupin

et al. 2015

IEEE Trans. Geosci. Remote Sensing

382

301

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Section: Discussionmentioning

confidence: 99%

NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising

Deledalle

Dénis

Tupin

et al. 2015

IEEE Trans. Geosci. Remote Sensing

382

301

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“…NLM algorithm corrects the noisy image rather than separates the noise (oscillatory) from true image (smooth), as is the case of DWT. In recent years many variants of NLM have been developed as for example Probabilistic NLM (PNLM) (Wu et al, 2013).…”

Section: Non-local Meanmentioning

confidence: 99%

“…For example, Ville and Kocher (2009) suggest SUREbased optimization of parameters selection. In our research we took general values for the parameters as given in (Wu et al, 2013) …”

Section: Nlm Parametersmentioning

confidence: 99%

“…The problem is pointed out in (Duval et al, 2011), but no clear reasoning exist for this inadequacy. Wu et al (2013) developed a new probabilistic weight function and showed that PNLM denoised results outperform the NLM. Besides, the PNLM works for any types of noise and the NLM assumes the Gaussian noise with zero mean and unknown variance.…”

Section: Nlm Versus Pnlmmentioning

confidence: 99%

“…Penalised low threshold is computed for alpha of 2. Parameters for NLM and PNLM are taken from (Wu et al, 2013), where the patch size is 7 and search window 21 pixels. The weights are computed using the known noise level σ.…”

Section: Experimental Workflowmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of Wavelet and Non-Local Mean Denoising of Terrestrial Laser Scanning Data for Small-Scale Joint Roughness Estimation

Bitenc

Kieffer

Khoshelham

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Terrestrial Laser Scanning (TLS) is a well-known remote sensing tool that enables precise 3D acquisition of surface morphology from distances of a few meters to a few kilometres. The morphological representations obtained are important in engineering geology and rock mechanics, where surface morphology details are of particular interest in rock stability problems and engineering construction. The actual size of the discernible surface detail depends on the instrument range error (noise effect) and effective data resolution (smoothing effect). Range error can be (partly) removed by applying a denoising method. Based on the positive results from previous studies, two denoising methods, namely 2D wavelet transform (WT) and non-local mean (NLM), are tested here, with the goal of obtaining roughness estimations that are suitable in the context of rock engineering practice. Both methods are applied in two variants: conventional Discrete WT (DWT) and Stationary WT (SWT), classic NLM (NLM) and probabilistic NLM (PNLM). The noise effect and denoising performance are studied in relation to the TLS effective data resolution. Analyses are performed on the reference data acquired by a highly precise Advanced TOpometric Sensor (ATOS) on a 20x30 cm rock joint sample. Roughness ratio is computed by comparing the noisy and denoised surfaces to the original ATOS surface. The roughness ratio indicates the success of all denoising methods. Besides, it shows that SWT oversmoothes the surface and the performance of the DWT, NLM and PNLM vary with the noise level and data resolution. The noise effect becomes less prominent when data resolution decreases.

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Attenuating noise from computed tomography medical images using a coefficients‐driven total variation denoising algorithm

Al-Ameen

Sulong

2014

Int J Imaging Syst Tech

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The aim of image denoising is to recover a visually accepted image from its noisy observation with as much detail as possible. The noise exists in computed tomography images due to hardware errors, software faults and/or low radiation dose. Because of noise, the analysis and extraction of accurate medical information is a challenging task for specialists. Therefore, a novel modification on the total variational denoising algorithm is proposed in this article to attenuate the noise from CT images and provide a better visual quality. The newly developed algorithm can properly detect noise from the other image components using four new noise distinguishing coefficients and reduce it using a novel minimization function. Moreover, the proposed algorithm has a fast computation speed, a simple structure, a relatively low computational cost and preserves the small image details while reducing the noise efficiently. Evaluating the performance of the proposed algorithm is achieved through the use of synthetic and real noisy images. Likewise, the synthetic images are appraised by three advanced accuracy methods -Gradient Magnitude Similarity Deviation (GMSD), Structural Similarity (SSIM) and Weighted Signal-to-Noise Ratio (WSNR). The empirical results exhibited significant improvement not only in noise reduction but also in preserving the minor image details. Finally, the proposed algorithm provided satisfying results that outperformed all the comparative methods.

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Probabilistic Non-Local Means

Cited by 78 publications

References 17 publications

NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising

NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising

Evaluation of Wavelet and Non-Local Mean Denoising of Terrestrial Laser Scanning Data for Small-Scale Joint Roughness Estimation

Attenuating noise from computed tomography medical images using a coefficients‐driven total variation denoising algorithm

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