Principal Neighborhood Dictionaries for Nonlocal Means Image Denoising

Taşdizen, Tolga

doi:10.1109/tip.2009.2028259

Cited by 208 publications

(128 citation statements)

References 32 publications

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“…Denoising is achieved by averaging pixel values weighted by the similarity of neighborhoods of each pixel in an image (Tasdizen, 2009). The conventional similarity weight is a degree of the similarity between neighborhoods of each pixel in an image (Yousif and Ban, 2014).…”

Section: Similarity Weight Image For Change Detectionmentioning

confidence: 99%

A Similarity Weight-based Method to Detect Damage Induced by a Tsunami

Jeon¹,

Kim²,

Kim³

2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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Among the various remote sensing sensors compared to the electro-optical sensors, SAR (Synthetic Aperture Radar) is very suitable for assessing damaged areas induced by disaster events owing to its all-weather day and night acquisition capability and sensitivity to geometric variables. The conventional CD (Change Detection) method that uses two-date data is typically used for mapping damage over extensive areas in a short time, but because data from only two dates are used, the information used in the conventional CD is limited. In this paper, we propose a novel CD method that is extended to use data consisting of two pre-disaster SAR data and one post-disaster SAR data. The proposed CD method detects changes by using a similarity weight image derived from the neighborhood information of a pixel in the data from the three dates. We conducted an experiment using three single polarization ALOS PALSAR (Advanced Land Observing Satellite/Phased Array Type L-Band) data collected over Miyagi, Japan which was seriously damaged by the 2011 east Japan tsunami. The results demonstrated that the mapping accuracy for damaged areas can be improved by about 26% with an increase of the g-mean compared to the conventional CD method. These improved results prove the performance of our proposed CD method and show that the proposed CD method is more suitable than the conventional CD method for detecting damaged areas induced by disaster. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Section: Similarity Weight Image For Change Detectionmentioning

confidence: 99%

A Similarity Weight-based Method to Detect Damage Induced by a Tsunami

Jeon¹,

Kim²,

Kim³

2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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“…Weighted Euclidean distance between two patches at one line (NLM-Pa using sum of invariant lines), see (22) …”

Section: L2mentioning

confidence: 99%

“…The window Ω x is called the search window at x and, for simplicity and faster computations, in [1] a choice of a square shape of fixed size is made while, according to authors, the search window should cover the entire image plane, hence the non-local nature of the algorithm. However, it has been reported that using for NLM a neighborhood instead of the whole image plane allows to increase the denoising performance [12,13,21,22], see also the discussion in [9] and the specific study in [19] where it is experimentally established that the optimal window size D is very small, when using a variant of the pixelwise NLM. As this present article will establish the best parameters, it will give an answer for the original pixelwise NLM.…”

Section: Introductionmentioning

confidence: 99%

Parameter-Free Fast Pixelwise Non-Local Means Denoising

Froment¹

2014

Image Processing on Line

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This article proposes a fast and open-source implementation of the well-known Non-Local Means (NLM) denoising algorithm, in its original pixelwise formulation. The fast implementation is based on the computation of patch distances using sums of lines that are invariant under a patch shift. The optimal parameters of NLM (in the average peak signal to noise ratio -PSNR -sense) are computed from an image database, thereby leading to a parameter-free NLM implementation. Comparison is performed with the parameter-free blockwise NLM implementation already proposed in IPOL journal by Buades, Coll and Morel. As expected the blockwise implementation offers better PSNR, at least when the noise standard deviation is large enough, but there is no significant difference in quality when performing visual inspection. The highlight is that the proposed parameter-free pixelwise NLM implementation is faster than the patchwise one by a factor of 6 to 49. Source CodeThe reviewed source code and documentation for the parameter-free fast pixelwise NLM algorithm are available from the web page of this article 1 . Compilation and usage instructions are included in the README.txt file of the archive.

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“…Similar speed-ups could be achieved for NLM reconstructions. Additional software approaches, for example, methods proposed previously (53,54), can be used in order to speed up computation of the weights for the reconstruction by selectively choosing the neighborhoods within the search window.…”

Section: Computation Timementioning

confidence: 99%

Reconstruction of 3D dynamic contrast‐enhanced magnetic resonance imaging using nonlocal means

Adluru

Taşdizen

Schabel

et al. 2010

Magnetic Resonance Imaging

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Purpose: To develop and test a nonlocal means-based reconstruction algorithm for undersampled 3D dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of tumors. Materials and Methods:We propose a reconstruction technique that is based on the recently proposed nonlocal means (NLM) filter which can relax trade-offs in spatial and temporal resolutions in dynamic imaging. Unlike the original application of NLM for image denoising, the MR reconstruction framework here can offer high-quality images from undersampled k-space data. The method is based on enforcing similarity constraints in terms of neighborhoods of pixels rather than individual pixels. The method was applied on undersampled 3D DCE imaging of breast and brain tumor datasets and the results were compared to sliding window reconstructions and to a compressed sensing method using total variation constraints on the images.Results: Undersampling factors of up to five were obtained with the proposed approach while preserving the spatial and temporal characteristics. The NLM reconstruction method offered improved performance over the sliding window and the total variation constrained reconstruction techniques. Conclusion:The reconstruction framework here can give high-quality images from undersampled DCE MRI data and has the potential to improve the quality of DCE tumor imaging.

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Principal Neighborhood Dictionaries for Nonlocal Means Image Denoising

Cited by 208 publications

References 32 publications

A Similarity Weight-based Method to Detect Damage Induced by a Tsunami

A Similarity Weight-based Method to Detect Damage Induced by a Tsunami

Parameter-Free Fast Pixelwise Non-Local Means Denoising

Reconstruction of 3D dynamic contrast‐enhanced magnetic resonance imaging using nonlocal means

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