Parameter-Free Fast Pixelwise Non-Local Means Denoising

Froment, Jacques

doi:10.5201/ipol.2014.120

Cited by 58 publications

(24 citation statements)

References 22 publications

(61 reference statements)

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“…A different acceleration strategy consists in comparing the projections of patches onto a low-dimensional subspace, e.g., via a singular value decomposition (Orchard et al 2008) or principal component analysis (Tasdizen 2009). Further references concerning fast variants of NL-means are provided by Froment (2014). Kervrann and Boulanger (2006) propose an extension of NL-means where the filtering parameters as well as the patch size vary spatially depending on the image content, while Salmon (2010) presents a numerical study on the selection of the search-neighborhood size, and a new criterion to set the weight w WIN (x 1 , x 1 ) for the central pixel.…”

Section: Extensions Of the Basic Nl-means Algorithmmentioning

confidence: 99%

“…Hence, fast implementations and variants of NL-means adopt strategies such as the integral-images method Darbon et al 2008;Froment 2014) or criteria for restricting the computation of weights to the most similar patches only. For instance, Buades et al (2005) present a multiscale solution where similar patches are first searched within a sub-sampled image, and then the weights are refined on the full-size noisy image, where the filtering is also performed.…”

Section: Extensions Of the Basic Nl-means Algorithmmentioning

confidence: 99%

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Foveated Nonlocal Self-Similarity

Foi

Boracchi

2016

Int J Comput Vis

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When we gaze a scene, our visual acuity is maximal at the fixation point (imaged by the fovea, the central part of the retina) and decreases rapidly towards the periphery of the visual field. This phenomenon is known as foveation. We investigate the role of foveation in nonlocal image filtering, installing a different form of self-similarity: the foveated self-similarity. We consider the image denoising problem as a simple means of assessing the effectiveness of descriptive models for natural images and we show that, in nonlocal image filtering, the foveated self-similarity is far more effective than the conventional windowed self-similarity. To facilitate the use of foveation in nonlocal imaging algorithms, we develop a general framework for designing foveation operators for patches by means of spatially variant blur. Within this framework, we construct several parametrized families of operators, including anisotropic ones. Strikingly, the foveation operators enabling the best denoising performance are the radial ones, in complete agreement with the orientation preference of the human visual system

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Section: Extensions Of the Basic Nl-means Algorithmmentioning

confidence: 99%

Section: Extensions Of the Basic Nl-means Algorithmmentioning

confidence: 99%

Foveated Nonlocal Self-Similarity

Foi

Boracchi

2016

Int J Comput Vis

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“…A faster sort algorithm can be potentially used to speed OWF and to get timings closer to those obtained with BM3D [11]. Moreover, we did not evaluate all the possible implementations as described for instance in [61,62]. Note that OWF has been recently implemented in parallel since every patch can be processed independently.…”

Section: Computings and Algorithm Implementationmentioning

confidence: 99%

Nonlocal Means and Optimal Weights for Noise Removal

Jin¹,

Grama²,

Kervrann³

et al. 2017

SIAM J. Imaging Sci.

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In this paper, a new denoising algorithm to deal with the additive white Gaussian noise model is described. In the line of work of the Non-Local means approach, we propose an adaptive estimator based on the weighted average of observations taken in a neighborhood with weights depending on the similarity of local patches. The idea is to compute adaptive weights that best minimize an upper bound of the pointwise L 2 risk. In the framework of adaptive estimation, we show that the "oracle" weights are optimal if we consider triangular kernels instead of the commonly-used Gaussian kernel. Furthermore, we propose a way to automatically choose the spatially varying smoothing parameter for adaptive denoising. Under conventional minimal regularity conditions, the obtained estimator converges at the usual optimal rate. The implementation of the proposed algorithm is also straightforward and the simulations show that our algorithm improves significantly the classical NL-means and is competitive when compared to the more sophisticated NL-means filters both in terms of PSNR values and visual quality.

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“…We tested and compared the effect of different denoising methods on image quality for small subsets gained from the large temporal median image (see Figure 9): the non-local means denoising [69][70][71][72], total variation chambolle [73], total variation bregman [74], and bilateral filter [75]. On the basis of visual perception, we considered the bilateral denoising and non-local means filter as the most suitable methods for sharpening the linear details such as dikes and levees located between adjoining ponds and improve the identification of single ponds.…”

Section: Edge Sharpeningmentioning

confidence: 99%

Large-Scale Assessment of Coastal Aquaculture Ponds with Sentinel-1 Time Series Data

2017

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Abstract:We present an earth observation based approach to detect aquaculture ponds in coastal areas with dense time series of high spatial resolution Sentinel-1 SAR data. Aquaculture is one of the fastest-growing animal food production sectors worldwide, contributes more than half of the total volume of aquatic foods in human consumption, and offers a great potential for global food security. The key advantages of SAR instruments for aquaculture mapping are their all-weather, day and night imaging capabilities which apply particularly to cloud-prone coastal regions. The different backscatter responses of the pond components (dikes and enclosed water surface) and aquaculture's distinct rectangular structure allow for separation of aquaculture areas from other natural water bodies. We analyzed the large volume of free and open Sentinel-1 data to derive and map aquaculture pond objects for four study sites covering major river deltas in China and Vietnam. SAR image data were processed to obtain temporally smoothed time series. Terrain information derived from DEM data and accurate coastline data were utilized to identify and mask potential aquaculture areas. An open source segmentation algorithm supported the extraction of aquaculture ponds based on backscatter intensity, size and shape features. We were able to efficiently map aquaculture ponds in coastal areas with an overall accuracy of 0.83 for the four study sites. The approach presented is easily transferable in time and space, and thus holds the potential for continental and global mapping.

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Parameter-Free Fast Pixelwise Non-Local Means Denoising

Cited by 58 publications

References 22 publications

Foveated Nonlocal Self-Similarity

Foveated Nonlocal Self-Similarity

Nonlocal Means and Optimal Weights for Noise Removal

Large-Scale Assessment of Coastal Aquaculture Ponds with Sentinel-1 Time Series Data

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