Nonlocal Means With Dimensionality Reduction and SURE-Based Parameter Selection

Ville, Dimitri Van De; Kocher, Michel

doi:10.1109/tip.2011.2121083

Cited by 97 publications

(52 citation statements)

References 31 publications

(31 reference statements)

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“…(2010) improved this approach by grouping similar patches before PCA decomposition and iterated the process to obtain a higher noise reduction. PCA has been also used to robustly compute patch similarities within a Non-local means framework (Van de Ville and Kocher, 2010;Zhang et al, 2013;Zhang et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

MRI noise estimation and denoising using non-local PCA

Manjón

Coupé²,

Buades³

2015

Medical Image Analysis

151

122

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This paper proposes a novel method for MRI denoising that exploits both the sparseness and self-similarity properties of the MR images. The proposed method is a two-stage approach that first filters the noisy image using a non local PCA thresholding strategy by automatically estimating the local noise level present in the image and second uses this filtered image as a guide image within a rotationally invariant non-local means filter. The proposed method internally estimates the amount of local noise presents in the images that enables applying it automatically to images with spatially varying noise levels and also corrects the Rician noise induced bias locally. The proposed approach has been compared with related state-of-the-art methods showing competitive results in all the studied cases.3

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Section: Introductionmentioning

confidence: 99%

MRI noise estimation and denoising using non-local PCA

Manjón

Coupé²,

Buades³

2015

Medical Image Analysis

151

122

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“…Alternatively, we may use Stein's unbiased risk estimator, which is widely used as an efficient parameter selection method [17][18][19] , to derive the adaptive parameter. In addition, we could reduce the computational burden by optimizing the whole process further, particularly the available speed-up methods for computing nonlocal weights.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Image interpolation based on nonlocal self-similarity

Guo¹,

Zhang²,

Liu³

et al. 2014

ScienceAsia

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This paper presents an efficient method for the resolution enhancement of low resolution (LR) images. Inspired by the great success of nonlocal filtering, the proposed method interpolates a missing pixel by a nonlocal estimation which takes into account the self-similarity of images and the geometric duality between LR images and high resolution (HR) images. This method firstly exploits the SAI method to generate an initial HR image of the given LR image. Then the initial HR image is further improved by a nonlocal estimation that uses the given LR image as a guiding image. Pixel correction is applied to reduce the distortion of the HR estimate. Experimental results demonstrate that the proposed method provides competitive performance.

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“…The proposed BSS method put additional .3 to 1.1 dB on the best PSNR scores and 2% to 8% on the best SSIM score of using the standard NLM CPW. It is worthwhile to point out these gains on the best standard NLM scores are not trivial, and to some extend these BSS scores with simple shrinkage estimations are comparable to or better than more complicated NLM variants, for examples the linear expansion with six NLMs (see Table II in [12]), and the multi-patch NLMs (see Table 5 in [1]), both of which requires multi-rounds of NLM denoising. Fig.…”

Section: Methodsmentioning

confidence: 98%

Fast blockwise SURE shrinkage for image denoising

Tracey

Natarajan

et al. 2014

Signal Processing

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In this letter, we investigate the shrinkage problem for the non-local means (NLM) image denoising.In particular, we derive the closed-form of the optimal blockwise shrinkage for NLM that minimizes the Stein's unbiased risk estimator (SURE). We also propose a constant complexity algorithm allowing fast blockwise shrinkage. Simulation results show that the proposed blockwise shrinkage method improves NLM performance in attaining higher peak signal noise ratio (PSNR) and structural similarity index Index TermsImage Denoising, Non-Local Means, Adaptive Algorithm, Shrinkage Estimator, Stein's Unbiased Risk EstimatorBrian Tracey and Joseph P. Noonan are with the department of electrical and computer engineering, Tufts university, 161 College Ave, Medford, MA 02155; e-mail: ywu03@ece.tufts.edu. Premkumar Natarajan is with the Raytheon BBN technologies, 10 Moulton St., Cambridge, MA 02138. Yue Wu was with the department of electrical and computer engineering at Tufts university, but is now with the Raytheon BBN technologies.

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Nonlocal Means With Dimensionality Reduction and SURE-Based Parameter Selection

Cited by 97 publications

References 31 publications

MRI noise estimation and denoising using non-local PCA

MRI noise estimation and denoising using non-local PCA

Image interpolation based on nonlocal self-similarity

Fast blockwise SURE shrinkage for image denoising

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