Postprocessing of Compressed Images via Sequential Denoising

Dar, Yehuda; Bruckstein, Alfred M.; Elad, Michael; Giryes, Raja

doi:10.1109/tip.2016.2558825

Cited by 66 publications

(46 citation statements)

References 39 publications

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“…The Plug-and-Play (PnP) ADMM is a variant of the alternating direction method of multiplier (ADMM) algorithm. Since its introduction in 2013 [1], the algorithm has demonstrated extremely promising results in image restoration and signal recovery problems [2]- [8]. However, despite the enormous number of applications and several studies on its convergence [8]- [10], it is generally unclear why the algorithm is performing so well.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Plug-and-Play ADMM: A Graph Signal Processing Perspective

Chan

2019

IEEE Trans. Comput. Imaging

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The Plug-and-Play (PnP) ADMM algorithm is a powerful image restoration framework that allows advanced image denoising priors to be integrated into physical forward models to generate high quality image restoration results. However, despite the enormous number of applications and several theoretical studies trying to prove convergence by leveraging tools in convex analysis, very little is known about why the algorithm is doing so well. The goal of this paper is to fill the gap by discussing the performance of PnP ADMM. By restricting the denoisers to the class of graph filters under a linearity assumption, or more specifically the symmetric smoothing filters, we offer three contributions: (1) We show conditions under which an equivalent maximum-a-posteriori (MAP) optimization exists, (2) we present a geometric interpretation and show that the performance gain is due to an intrinsic pre-denoising characteristic of the PnP prior, (3) we introduce a new analysis technique via the concept of consensus equilibrium, and provide interpretations to problems involving multiple priors.

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Section: Introductionmentioning

confidence: 99%

Performance Analysis of Plug-and-Play ADMM: A Graph Signal Processing Perspective

Chan

2019

IEEE Trans. Comput. Imaging

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“…For lowest SNR settings, participants reported a pronounced "musical noise" effect. Some complementary techniques like multichannel Wiener filter or sequential denoising [22] could be investigated in a further study to address this issue. Besides, during informal pilot listening tests, we noticed that the AUDASCITY estimate feels "richer", in the sense that it recovers both low and high frequency content (as opposed to BT which is severely low-pass filtered).…”

Section: Perceptual Resultsmentioning

confidence: 99%

AUDASCITY: AUdio denoising by adaptive social CosparsITY

Gaultier

Kitić

Bertin

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-This work aims at introducing a new algorithm, AUDASCITY, and comparing its performance to the timefrequency block thresholding algorithm for the ill-posed problem of audio denoising. We propose a heuristics which combines time-frequency structure, cosparsity, and an adaptive scheme to denoise audio signals corrupted with white noise. We report that AUDASCITY outperforms state-of-the-art for each numerical comparison. While there is still room for some perceptual improvements, AUDASCITY's usefulness is shown when used as a front-end for a classification task.

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“…As in the univariate case, we notice that ADMM converges when the regularizer R is chosen as nonconvex. More remarkably, as observed in many contexts, e.g., [67], [13], [65], [14], [56], [8], replacing the minimization problem in (39) by the solution of a Gaussian denoiser -a scheme known as plug-and-play -leads to appealing results. The resulting algorithm, coined MUlti-channel LOgarithm with Gaussian denoising (MuLoG), is given aŝ…”

Section: F Adaptation To Advanced Filtersmentioning

confidence: 99%

MuLoG, or How to Apply Gaussian Denoisers to Multi-Channel SAR Speckle Reduction?

Deledalle

Denis

Tabti

et al. 2017

IEEE Trans. on Image Process.

128

100

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Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) imaging. Since most current and planned SAR imaging satellites operate in polarimetric, interferometric, or tomographic modes, SAR images are multi-channel and speckle reduction techniques must jointly process all channels to recover polarimetric and interferometric information. The distinctive nature of SAR signal (complex-valued, corrupted by multiplicative fluctuations) calls for the development of specialized methods for speckle reduction. Image denoising is a very active topic in image processing with a wide variety of approaches and many denoising algorithms available, almost always designed for additive Gaussian noise suppression. This paper proposes a general scheme, called MuLoG (MUlti-channel LOgarithm with Gaussian denoising), to include such Gaussian denoisers within a multi-channel SAR speckle reduction technique. A new family of speckle reduction algorithms can thus be obtained, benefiting from the ongoing progress in Gaussian denoising, and offering several speckle reduction results often displaying method-specific artifacts that can be dismissed by comparison between results.

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Postprocessing of Compressed Images via Sequential Denoising

Cited by 66 publications

References 39 publications

Performance Analysis of Plug-and-Play ADMM: A Graph Signal Processing Perspective

Performance Analysis of Plug-and-Play ADMM: A Graph Signal Processing Perspective

AUDASCITY: AUdio denoising by adaptive social CosparsITY

MuLoG, or How to Apply Gaussian Denoisers to Multi-Channel SAR Speckle Reduction?

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