Secrets of image denoising cuisine

Lebrun, Marc; Colom, Miguel; Buades, Antoni; Morel, Jean‐Michel

doi:10.1017/s0962492912000062

Cited by 177 publications

(113 citation statements)

References 143 publications

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“…The various algorithms designed to solve this problem must rely on some prior knowledge about the model of the image, in order to differentiate it from the noise [18]. The vast majority of modern denoising algorithms are patch-based, frequencybased or a combination of the two.…”

Section: The Artifacts Of Denoising Algorithms and Their Interpretationmentioning

confidence: 99%

Data Adaptive Dual Domain Denoising: a Method to

Pierazzo¹,

Facciolo²

2017

Image Processing on Line

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This article presents DA3D (Data Adaptive Dual Domain Denoising), a "last step denoising" method that takes as input a noisy image and as a guide the result of any state-of-the-art denoising algorithm. The method performs frequency domain shrinkage on shape and dataadaptive patches. DA3D doesn't process all the image samples, which allows it to use large patches (64 × 64 pixels). The shape and data-adaptive patches are dynamically selected, effectively concentrating the computations on areas with more details, thus accelerating the process considerably. DA3D also reduces the staircasing artifacts sometimes present in smooth parts of the guide images. The effectiveness of DA3D is confirmed by extensive experimentation. DA3D improves the result of almost all state-of-the-art methods, and this improvement requires little additional computation time. Source CodeThe C++ source code, the code documentation, and the online demo are accessible at the IPOL web page of this article web site 1 Compilation and usage instruction are included in the README.txt file of the archive.

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Section: The Artifacts Of Denoising Algorithms and Their Interpretationmentioning

confidence: 99%

Data Adaptive Dual Domain Denoising: a Method to

Pierazzo¹,

Facciolo²

2017

Image Processing on Line

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“…learning, nonparametric, or empirical Bayes' methods). Most recently, the latter approach has become very popular, mainly using patch-based methods that exploit both local and nonlocal redundancies or 'self-similarities' in the images (Lebrun et al, 2012). A patch-based algorithm denoises each pixel by using knowledge of (a) the patch surrounding it and (b) the probability density of all existing patches.…”

Section: Image Denoisingmentioning

confidence: 99%

Advances in Image Pre-Processing to Improve Automated 3d Reconstruction

Ballabeni

Apollonio

Gaiani

et al. 2015

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

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KEY WORDS: Pre-processing, Filtering, Matching, Automation, Denoising, RGB to Gray, Color, 3D reconstruction ABSTRACT Tools and algorithms for automated image processing and 3D reconstruction purposes have become more and more available, giving the possibility to process any dataset of unoriented and markerless images. Typically, dense 3D point clouds (or texture 3D polygonal models) are produced at reasonable processing time. In this paper, we evaluate how the radiometric pre-processing of image datasets (particularly in RAW format) can help in improving the performances of state-of-the-art automated image processing tools. Beside a review of common pre-processing methods, an efficient pipeline based on color enhancement, image denoising, RGB to Gray conversion and image content enrichment is presented. The performed tests, partly reported for sake of space, demonstrate how an effective image pre-processing, which considers the entire dataset in analysis, can improve the automated orientation procedure and dense 3D point cloud reconstruction, even in case of poor texture scenarios.

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“…Some methods are devoted to the denoising problem [5]- [8], while others propose a more general framework for the solution of image inverse problems [9], [10], including inpainting, deblurring and zooming. The work by Lebrun et al [7], [11] presents a thorough analysis of several recent restoration methods, revealing their common roots and their relationship with the Bayesian approach.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, With an Application to HDR Imaging

Aguerrebere

Almansa

Delon

et al. 2017

IEEE Trans. Comput. Imaging

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Abstract-Recently, impressive denoising results have been achieved by Bayesian approaches which assume Gaussian models for the image patches. This improvement in performance can be attributed to the use of per-patch models. Unfortunately such an approach is particularly unstable for most inverse problems beyond denoising. In this work, we propose the use of a hyperprior to model image patches, in order to stabilize the estimation procedure. There are two main advantages to the proposed restoration scheme: Firstly it is adapted to diagonal degradation matrices, and in particular to missing data problems (e.g. inpainting of missing pixels or zooming). Secondly it can deal with signal dependent noise models, particularly suited to digital cameras. As such, the scheme is especially adapted to computational photography. In order to illustrate this point, we provide an application to high dynamic range imaging from a single image taken with a modified sensor, which shows the effectiveness of the proposed scheme.Index Terms-Non-local patch-based restoration, Bayesian restoration, Maximum a Posteriori, Gaussian Mixture Models, hyper-prior, conjugate distributions, high dynamic range imaging, single shot HDR, hierarchical models.

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Secrets of image denoising cuisine

Cited by 177 publications

References 143 publications

Data Adaptive Dual Domain Denoising: a Method to

Data Adaptive Dual Domain Denoising: a Method to

Advances in Image Pre-Processing to Improve Automated 3d Reconstruction

A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, With an Application to HDR Imaging

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