Adaptive denoising of 3D volumetric MR images using local variance based estimator

Das, Pabitra; Pal, Chandrajit; Chakrabarti, Amlan; Acharyya, Amit; Basu, Saumyajit

doi:10.1016/j.bspc.2020.101901

Cited by 12 publications

(2 citation statements)

References 47 publications

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“…When handling specific imaging types (e.g., microscopy [223,25,13,103,340,186,158,188], CT [164,48,318,316,70,314] and PET/SPECT imaging [51,82,105,227,343,266], and more), the algorithm design may require adequate adaptations to the data format (e.g. treating 3D volumes [294,336,324,64,178]) or to the way it is captured.…”

Section: Extensions Of Imagementioning

confidence: 99%

Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --

Elad¹,

Kawar²,

Vaksman³

2023

Preprint

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Image denoising -removal of additive white Gaussian noise from an image -is one of the oldest and most studied problems in image processing. An extensive work over several decades has led to thousands of papers on this subject, and to many well-performing algorithms for this task. Indeed, ten years ago, these achievements have led some researchers to suspect that "Denoising is Dead", in the sense that all that can be achieved in this domain has already been obtained. However, this turned out to be far from the truth, with the penetration of deep learning (DL) into the realm of image processing. The era of DL brought a revolution to image denoising, both by taking the lead in today's ability for noise suppression in images, and by broadening the scope of denoising problems being treated. Our paper starts by describing this evolution, highlighting in particular the tension and synergy that exist between classical approaches and modern Artificial Intelligence (AI) alternatives in design of image denoisers.The recent transitions in the field of image denoising go far beyond the ability to design better denoisers. In the second part of this paper we focus on recently discovered abilities and prospects of image denoisers. We expose the possibility of using image denoisers for service of other problems, such as regularizing general inverse problems and serving as the prime engine in diffusion-based image synthesis. We also unveil the (strange?) idea that denoising and other inverse problems might not have a unique solution, as common algorithms would have us believe. Instead, we describe constructive ways to produce randomized and diverse high perceptual quality results for inverse problems, all fueled by the progress that DL brought to image denoising. This is a survey paper, and its prime goal is to provide a broad view of the history of the field of image denoising and closely related topics in image processing. Our aim is to give a better context to recent discoveries, and to the influence of the AI revolution in our domain.

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Section: Extensions Of Imagementioning

confidence: 99%

Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --

Elad¹,

Kawar²,

Vaksman³

2023

Preprint

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“…In image denoising, additive Gaussian white noise level is an important parameter, but it is usually unknown in real-life images [ 1 , 2 ]. A denoising method with accurate noise levels may generate comfortable results with abundant richness [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Blind Additive Gaussian White Noise Level Estimation from a Single Image by Employing Chi-Square Distribution

Wang

Zhu

et al. 2022

Entropy

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The additive Gaussian white noise (AGWN) level in real-life images is usually unknown, for which the empirical setting will make the denoising methods over-smooth fine structures or remove noise incompletely. The previous noise level estimation methods are easily lost in accurately estimating them from images with complicated structures. To cope with this issue, we propose a novel noise level estimation scheme based on Chi-square distribution, including the following key points: First, a degraded image is divided into many image patches through a sliding window. Then, flat patches are selected by using a patch selection strategy on the gradient maps of those image patches. Next, the initial noise level is calculated by employing Chi-square distribution on the selected flat patches. Finally, the stable noise level is optimized by an iterative strategy. Quantitative, with association, to qualitative results of experiments on synthetic real-life images validate that the proposed noise level estimation method is effective and even superior to the state-of-the-art methods. Extensive experiments on noise removal using BM3D further illustrate that the proposed noise level estimation method is more beneficial for achieving favorable denoising performance with detail preservation.

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