Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data

Foi, Alessandro; Trimeche, Mejdi; Katkovnik, Vladimir; Егиазарян, Карен

doi:10.1109/tip.2008.2001399

Cited by 752 publications

(708 citation statements)

References 14 publications

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“…In particular, our non-local transform decomposition can be embedded within the estimation algorithm for heteroskedastic observations [7], which is relevant for modeling dealing with raw data from digital imaging sensors. Let us however mention that, when facing heteroskedasticity, to avoid additional bias the block sizes cannot be arbitrarily large.…”

Section: Discussionmentioning

confidence: 99%

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Noise variance estimation in nonlocal transform domain

Danielyan

Foi

2009

2009 International Workshop on Local and Non-Local Approximation in Image Processing

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We consider the estimation of the variance of an additive white Gaussian noise corrupting an image.In the proposed approach, we exploit the nonlocal selfsimilarity of images to achieve an improved separation of noise and signal. In particular, we utilize the same adaptive 3-D transform decomposition used in the BM3D (blockmatching and 3-D Þltering) denoising algorithm, where mutually similar blocks are stacked together and jointly processed. An adaptive-size portion of the high-frequency ends of the 3-D transform spectra is retained and used as input sample for a robust median estimator of the absolute deviation.Experimental analysis demonstrate a state-of-the-art accuracy of the proposed approach.

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Section: Discussionmentioning

confidence: 99%

“…The local smoothness or fast spectral decay of natural images are typical hypotheses to make the estimation possible, e.g., by measuring the noise statistics on some homogenous patches [1] or on the highestfrequency portion of the image spectrum [4], [7].…”

Section: Introductionmentioning

confidence: 99%

Noise variance estimation in nonlocal transform domain

Danielyan

Foi

2009

2009 International Workshop on Local and Non-Local Approximation in Image Processing

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“…In practice, an additive random term modeling the effect of noise has to be added in (1) [20]. We come back to the noisy case in Sect.…”

Section: Spectral Characterization Of Cfasmentioning

confidence: 99%

“…In practice, the AWGN assumption is not met; real noise is more accurately modeled as the sum of Gaussian and Poissonian noises [20]. Moreover, the pixel values are gamma corrected with respect to the photon counts output by the sensor [29] and this step of tone mapping modifies the noise characteristics.…”

Section: B Behavior In the Presence Of Noisementioning

confidence: 99%

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A New Color Filter Array With Optimal Properties for Noiseless and Noisy Color Image Acquisition

Condat

2011

IEEE Trans. on Image Process.

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Abstract-Digital color cameras acquire color images by means of a sensor on which a color filter array (CFA) is overlaid. The Bayer CFA dominates the consumer market, but there has been recently a renewed interest for the design of CFAs [2]-[6]. However, robustness to noise is often neglected in the design, though it is crucial in practice. In this work, we present a new 2 × 3-periodic CFA which provides, by construction, the optimal tradeoff between robustness to aliasing, chrominance noise and luminance noise. Moreover, a simple and efficient linear demosaicking algorithm is described, which fully exploits the spectral properties of the CFA. Practical experiments confirm the superiority of our design, both in noiseless and noisy scenarios.Index Terms-Color filter array (CFA), color imaging, demosaicking, digital camera pipeline, spatio-spectral sampling, luminance/chrominance basis, noise sensitivity.

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Image Denoising Algorithms: From Wavelet Shrinkage to Nonlocal Collaborative Filtering

Pižurica¹

2017

Wiley Encyclopedia of Electrical and Electronics Engineering

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This article presents an overview of image denoising algorithms ranging from wavelet shrinkage to patch‐based nonlocal processing. The focus is on the suppression of additive Gaussian noise (white and colored). A great attention is devoted to explaining the main underlying ideas and concepts of representative approaches, complemented by illustrative examples, accessible also to nonexperts in the field. A Bayesian perspective of wavelet shrinkage is given, with different instances of spatial context modeling (including local spatial activity indicators, Markov random fields, hidden Markov tree models, and Gaussian scale mixture models). Extensions to other transform domains (curvelets and other generalizations of wavelets) are addressed too, showing the benefits in terms of image quality. Patch‐based image denoising is illustrated with principles of nonlocal means filtering and collaborative filtering, explaining also the connections with dictionary learning. Some general notes on the performance comparison are given, by summarizing the benefits and limitations of various approaches against each other, and pointing to some of the current trends in the field.

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Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data

Cited by 752 publications

References 14 publications

Noise variance estimation in nonlocal transform domain

Noise variance estimation in nonlocal transform domain

A New Color Filter Array With Optimal Properties for Noiseless and Noisy Color Image Acquisition

Image Denoising Algorithms: From Wavelet Shrinkage to Nonlocal Collaborative Filtering

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