From patches to pixels in Non-Local methods: Weighted-average reprojection

Salmon, Joseph; Strozecki, Yann

doi:10.1109/icip.2010.5650780

Cited by 30 publications

(39 citation statements)

References 12 publications

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“…Here, indexes only those patches that include the th pixel of the image and position of the th pixel is dependent upon the patch being considered. Such a weighted least-square estimate obtained using the error variance from multiple estimates , dating at least back to [40], also forms the basis for aggregation in [12], [41], and [42]. In every case, such aggregation leads to an improved final pixel estimate compared with other naive approaches (e.g., simple averaging).…”

Section: Aggregating Multiple Pixel Estimatesmentioning

confidence: 99%

Patch-Based Near-Optimal Image Denoising

Chatterjee

Milanfar

2012

IEEE Trans. on Image Process.

266

183

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Abstract-In this paper, we propose a denoising method motivated by our previous analysis of the performance bounds for image denoising. Insights from that study are used here to derive a high-performance practical denoising algorithm. We propose a patch-based Wiener filter that exploits patch redundancy for image denoising. Our framework uses both geometrically and photometrically similar patches to estimate the different filter parameters. We describe how these parameters can be accurately estimated directly from the input noisy image. Our denoising approach, designed for near-optimal performance (in the mean-squared error sense), has a sound statistical foundation that is analyzed in detail. The performance of our approach is experimentally verified on a variety of images and noise levels.The results presented here demonstrate that our proposed method is on par or exceeding the current state of the art, both visually and quantitatively.Index Terms-Denoising bounds, image clustering, image denoising, linear minimum mean-squared-error (LMMSE) estimator, Wiener filter.

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Section: Aggregating Multiple Pixel Estimatesmentioning

confidence: 99%

Patch-Based Near-Optimal Image Denoising

Chatterjee

Milanfar

2012

IEEE Trans. on Image Process.

266

183

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“…Promising avenues also include the use of different linear projections of the neighborhoods (e.g., to improve invariance to some features) and further development of a spatially adaptive version of NLM, including speeding up the algorithm [12]. Finally, the reprojection method from [33] could be incorporated to improve visual quality once the optimal parameter set determined.…”

Section: Discussionmentioning

confidence: 99%

Nonlocal Means With Dimensionality Reduction and SURE-Based Parameter Selection

Ville

Kocher

2011

IEEE Trans. on Image Process.

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“…All the estimators coming from the different patches containing the pixel are averaged with weights representing the precision of the corresponding estimator. For some details see [18,17,6].…”

Section: From Patches To Images: Aggregation Proceduresmentioning

confidence: 99%

Gaussian Priors for Image Denoising

Delon

Houdard

2018

Denoising of Photographic Images and Video

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This chapter is dedicated to the study of Gaussian priors for patch-based image denoising. In the last twelve years, patch priors have been widely used for image restoration. In a Bayesian framework, such priors on patches can be used for instance to estimate a clean patch from its noisy version, via classical estimators such as the conditional expectation or the maximum a posteriori. As we will recall, in the case of Gaussian white noise, simply assuming Gaussian (or Mixture of Gaussians) priors on patches leads to very simple closed-form expressions for some of these estimators. Nevertheless, the convenience of such models should not prevail over their relevance. For this reason, we also discuss how these models represent patches and what kind of information they encode. The end of the chapter focuses on the different ways in which these models can be learned on real data. This stage is particularly challenging because of the curse of dimensionality. Through these different questions, we compare and connect several denoising methods using this framework.

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From patches to pixels in Non-Local methods: Weighted-average reprojection

Cited by 30 publications

References 12 publications

Patch-Based Near-Optimal Image Denoising

Patch-Based Near-Optimal Image Denoising

Nonlocal Means With Dimensionality Reduction and SURE-Based Parameter Selection

Gaussian Priors for Image Denoising

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