Gaussian-Cauchy mixture modeling for robust signal-dependent noise estimation

Azzari, Lucio; Foi, Alessandro

doi:10.1109/icassp.2014.6854626

Cited by 29 publications

(36 citation statements)

References 10 publications

(23 reference statements)

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“…Likewise, the simplest LS fitting method is not robust to outliers in the scatterplot. Therefore, the use of a better variance estimator and a better (e.g., robust) fitting algorithm [19] could further improve the estimation, so to possibly deal with highly textured images such as the example in Figure 7.…”

Section: Discussionmentioning

confidence: 99%

“…To evaluate the impact of the block size, we repeat the 4 A robust variant of [1] was recently published [19] while the present article was already in press. The variant models the scatterplot points as an adaptive mixture of Gaussian and Cauchy distributions, and thus yields more accurate results in cases with outliers such as that illustrated in Figure 7.…”

Section: Experiments On Camera Raw Imagesmentioning

confidence: 99%

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Indirect Estimation of Signal-Dependent Noise With Nonadaptive Heterogeneous Samples

Azzari

Foi

2014

IEEE Trans. on Image Process.

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Experiments On Camera Raw Imagesmentioning

confidence: 99%

Indirect Estimation of Signal-Dependent Noise With Nonadaptive Heterogeneous Samples

Azzari

Foi

2014

IEEE Trans. on Image Process.

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“…Hashemi et al [16] studied behavior of the Bayesian estimator for noisy GGD data, and showed that this estimator can be well estimated with a simple shrinkage function. Azzari et al [17] introduced an adaptive Gaussian-Cauchy mixture modeling for the likelihood of pairwise mean/standard deviation scatter points found when estimating signal-dependent noise. The maximization of the likelihood is used to identify the noise model parameters, following an adaptive mixture parameter that controls the balance between the Gaussian and the heavy-tailed Cauchy.…”

Section: Related Workmentioning

confidence: 99%

“…Although seemingly very different, they all share the same property: to keep the meaningful edges and remove less meaningful ones. The existing image denoising work can be roughly divided into Nonlocal Methods [3][4][5], Random Fields [6][7][8], Bilateral Filtering [9][10][11], Anisotropic Diffusion [12,13], and Statistical Model [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. In addition, many authors have developed image denoising algorithms based on support vector machine (SVM) classification [14].…”

Section: Introductionmentioning

confidence: 99%

Extended Shearlet HMT Model-Based Image Denoising Using BKF Distribution

et al. 2015

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Images are often corrupted by noise in the procedures of image acquisition and transmission. It is a challenging work to design an edge-preserving image denoising scheme. Extended discrete Shearlet transform (extended DST) is an effective multi-scale and multi-direction analysis method; it not only can exactly compute the Shearlet coefficients based on a multiresolution analysis, but also can represent images with very few coefficients. In this paper, we propose a new image denoising approach in extended DST domain, which combines hidden Markov tree (HMT) model and Bessel K Form (BKF) distribution. Firstly, the marginal statistics of extended DST coefficients are studied, and their distribution is analytically calculated by modeling extended DST coefficients with BKF probability density function. Then, an extended Shearlet HMT model is established for capturing the intra-scale, inter-scale, and cross-orientation coefficients dependencies. Finally, an image denoising approach based on the extended Shearlet HMT model is presented. Extensive experimental results demonstrate that our extended Shearlet HMT denoising approach can obtain better performances in terms of both subjective and objective evaluations than other state-of-theart HMT denoising techniques. Especially, the proposed approach can preserve edges very well while removing noise.

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“…For some textured reference image, its results are even totally wrong. It should be noted that a robust version of their algorithm has been recently proposed [4]. On the other hand, the proposed algorithm gives quite reliable estimations of g (the actual value is within the estimated confidence interval, which is quite small).…”

Section: Synthetic Datamentioning

confidence: 99%

Measuring the Noise of Digital Imaging Sensors by Stacking Raw Images Affected by Vibrations and Illumination Flickering

Sur¹,

Grédiac²

2015

SIAM J. Imaging Sci.

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Abstract. This paper discusses camera noise estimation from a series of raw images of an arbitrary natural static scene, acquired with the same camera settings. Although it seems natural to characterize noise from the random time fluctuation of pixel intensity, it turns out that these fluctuations may also be caused by illumination flickering and mechanical microvibrations affecting the camera. In this context, the contributions are twofold. First, a theoretical model of image formation in the presence of illumination flickering and of vibrations is discussed. This parametric model is based on a Cox process. It is shown that illumination flickering changes the standard affine relation between noise variance and average intensity to a quadratic relation. Second, under these conditions an algorithm is proposed to estimate the main parameters governing sensor noise, namely the gain, the offset, and the readout noise. The rolling shutter effect, which potentially affects the output of any focal-plane shutter camera, is also considered. Experiments show that this simple method gives results consistent with the photon transfer method, which needs a special experimental setting and several data acquisitions, and with an algorithm based on a single image. The main practical result is to show that flickering, which is generally considered as an artifact, here plays a positive role since it finally enables us to estimate any of the sensor parameters.

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Gaussian-Cauchy mixture modeling for robust signal-dependent noise estimation

Cited by 29 publications

References 10 publications

Indirect Estimation of Signal-Dependent Noise With Nonadaptive Heterogeneous Samples

Indirect Estimation of Signal-Dependent Noise With Nonadaptive Heterogeneous Samples

Extended Shearlet HMT Model-Based Image Denoising Using BKF Distribution

Measuring the Noise of Digital Imaging Sensors by Stacking Raw Images Affected by Vibrations and Illumination Flickering

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