Noise variance estimation in nonlocal transform domain

Danielyan, Aram; Foi, Alessandro

doi:10.1109/lnla.2009.5278404

Cited by 45 publications

(23 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nowadays people use the TID2008 for some other purposes than it was originally created [49,50]. Since this image database already contains noisy and reference images, it can be also exploited for testing image filtering efficiency.…”

Section: Tampere Image Database 2008 (Tid) and Used Noise Modelsmentioning

confidence: 99%

Efficiency analysis of color image filtering

Fevralev

Ponomarenko

Lukin

et al. 2011

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

This article addresses under which conditions filtering can visibly improve the image quality. The key points are the following. First, we analyze filtering efficiency for 25 test images, from the color image database TID2008. This database allows assessing filter efficiency for images corrupted by different noise types for several levels of noise variance. Second, the limit of filtering efficiency is determined for independent and identically distributed (i.i.d.) additive noise and compared to the output mean square error of state-of-the-art filters. Third, component-wise and vector denoising is studied, where the latter approach is demonstrated to be more efficient. Fourth, using of modern visual quality metrics, we determine that for which levels of i.i.d. and spatially correlated noise the noise in original images or residual noise and distortions because of filtering in output images are practically invisible. We also demonstrate that it is possible to roughly estimate whether or not the visual quality can clearly be improved by filtering.

show abstract

Section: Tampere Image Database 2008 (Tid) and Used Noise Modelsmentioning

confidence: 99%

Efficiency analysis of color image filtering

Fevralev

Ponomarenko

Lukin

et al. 2011

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…In its prototype implementation, our algorithm is limited by the accuracy of the MAD estimator and thus cannot reach the accuracy of algorithms (e.g., [17]) that adopt more sophisticated estimators for the estimation of the variance. Likewise, the simplest LS fitting method is not robust to outliers in the scatterplot.…”

Section: Discussionmentioning

confidence: 99%

“…In fact, the advanced methods [17] and [18] are developed for AWGN estimation. As shown in our theoretical and experimental analysis, the fact that the variance of the noise is not constant (heteroskedasticity), and depends instead on the signal, does not per se imply an additional need for adaptive segmentation.…”

Section: Discussionmentioning

confidence: 99%

Indirect Estimation of Signal-Dependent Noise With Nonadaptive Heterogeneous Samples

Azzari

Foi

2014

IEEE Trans. on Image Process.

Self Cite

View full text Add to dashboard Cite

Abstract-We consider the estimation of signal-dependent noise from a single image. Unlike conventional algorithms that build a scatterplot of local mean-variance pairs from either small or adaptively selected homogeneous data samples, our proposed approach relies on arbitrarily large patches of heterogeneous data extracted at random from the image. We demonstrate the feasibility of our approach through an extensive theoretical analysis based on mixture of Gaussian distributions. A prototype algorithm is also developed in order to validate the approach on simulated data as well as on real camera raw images.

show abstract

“…Even though this would strictly require the independence of the data, i.e. non overlapping cubes, 12 we have experimentally found that the potential underestimation due to overlaps does not significantly affect the final denoising quality.…”

Section: Gaussian-distributed Datamentioning

confidence: 93%

“…which, similarly to (12), give an estimate of the total residual noise variance of the corresponding Wiener filtered group. …”

Section: The Set Of Coordinates Sŷmentioning

confidence: 99%

Nonlocal transform-domain denoising of volumetric data with groupwise adaptive variance estimation

Maggioni

Foi

2012

SPIE Proceedings

Self Cite

View full text Add to dashboard Cite

We propose an extension of the BM4D volumetric filter to the denoising of data corrupted by spatially nonuniform noise. BM4D implements the grouping and collaborative filtering paradigm, where similar cubes of voxels are stacked into a four-dimensional "group". Each group undergoes a sparsifying four-dimensional transform, that exploits the local correlation among voxels in each cube and the nonlocal correlation between corresponding voxels of different cubes. Thus, signal and noise are effectively separated in transform domain. In this work we take advantage of the sparsity induced by the four-dimensional transform to provide a spatially adaptive estimation of the local noise variance by applying a robust median estimator of the absolute deviation to the spectrum of each filtered group. The adaptive variance estimates are then used during coefficients shrinkage. Finally, the inverse four-dimensional transform is applied to the filtered group, and each individual cube estimate is adaptively aggregated at its original location.Experiments on medical data corrupted by spatially varying Gaussian and Rician noise demonstrate the efficacy of the proposed approach in volumetric data denoising. In case of magnetic resonance signals, the adaptive variance estimate can be also used to compensate the estimation bias due to the non-zero-mean errors of the Rician-distributed data.

show abstract

Noise variance estimation in nonlocal transform domain

Cited by 45 publications

References 15 publications

Efficiency analysis of color image filtering

Efficiency analysis of color image filtering

Indirect Estimation of Signal-Dependent Noise With Nonadaptive Heterogeneous Samples

Nonlocal transform-domain denoising of volumetric data with groupwise adaptive variance estimation

Contact Info

Product

Resources

About