The Noise Clinic: a Blind Image Denoising Algorithm

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

doi:10.5201/ipol.2015.125

Cited by 157 publications

(131 citation statements)

References 25 publications

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“…‚ Noise Clinic [43][44][45][46]: It is the conjunction of a noise estimation method and of a denoising method. Noise estimation is with an extension of [47] method to be able to estimate signal-dependent noise, followed by multiscale NL-Bayes denoising method.…”

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confidence: 99%

An Advanced Pre-Processing Pipeline to Improve Automated Photogrammetric Reconstructions of Architectural Scenes

et al. 2016

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Automated image-based 3D reconstruction methods are more and more flooding our 3D modeling applications. Fully automated solutions give the impression that from a sample of randomly acquired images we can derive quite impressive visual 3D models. Although the level of automation is reaching very high standards, image quality is a fundamental pre-requisite to produce successful and photo-realistic 3D products, in particular when dealing with large datasets of images. This article presents an efficient pipeline based on color enhancement, image denoising, color-to-gray conversion and image content enrichment. The pipeline stems from an analysis of various state-of-the-art algorithms and aims to adjust the most promising methods, giving solutions to typical failure causes. The assessment evaluation proves how an effective image pre-processing, which considers the entire image dataset, can improve the automated orientation procedure and dense 3D point cloud reconstruction, even in the case of poor texture scenarios.

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confidence: 99%

An Advanced Pre-Processing Pipeline to Improve Automated Photogrammetric Reconstructions of Architectural Scenes

et al. 2016

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“…This requires a specific PCA model suitable for that type of noise. A multiscale version of [32] was proposed in [35] to handle structured noise, focusing on JPEG compression artifacts. The authors in [31] propose an extension of [32] for the denoising of images taking values on Riemannian manifolds.…”

Section: Related Workmentioning

confidence: 99%

Video Denoising via Empirical Bayesian Estimation of Space-Time Patches

Arias

Morel

2017

J Math Imaging Vis

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In this paper we present a new patch-based empirical Bayesian video denoising algorithm. The method builds a Bayesian model for each group of similar space-time patches. These patches are not motion compensated, and therefore avoid the risk of inaccuracies caused by motion estimation errors. The high dimensionality of spatio-temporal patches together with a limited number of available samples, poses challenges when estimating the statistics needed for an empirical Bayesian method. We therefore assume that groups of similar patches have a low intrinsic dimensionality, leading to a spiked covariance model. Based on theoretical results about the estimation of spiked covariance matrices, we propose estimators of the eigenvalues of the a priori covariance in high dimensional spaces as simple corrections of the eigenvalues of the sample covariance matrix. We demonstrate empirically that these estimators lead to better empirical Wiener filters. A comparison on classic benchmark videos demonstrates improved visual quality and an increased PSNR with respect to state-of-the-art video denoising methods.

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“…• Imagenomic Noiseware (Imagenomic LLC, 2012;Petrosyan, & Ghazaryan, 2006); • Adobe Camera RAW denoise (Schewe & Fraser, 2010 (Lebrun et al 2014;Colom et al 2014; • Color Block Matching 3D (CBM3D) filter (Dabov et al, 2007b) a color variant of Block Matching 3D (BM3D) filter (Dabov et al, 2007a). Following results of these experiments, an in-house solution was developed starting from the CBM3D method.…”

Section: Evaluated Methods and Proposed Approachmentioning

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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The Noise Clinic: a Blind Image Denoising Algorithm

Cited by 157 publications

References 25 publications

An Advanced Pre-Processing Pipeline to Improve Automated Photogrammetric Reconstructions of Architectural Scenes

An Advanced Pre-Processing Pipeline to Improve Automated Photogrammetric Reconstructions of Architectural Scenes

Video Denoising via Empirical Bayesian Estimation of Space-Time Patches

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

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