Patch-Based Near-Optimal Image Denoising

Chatterjee, Priyam; Milanfar, Peyman

doi:10.1109/tip.2011.2172799

Cited by 268 publications

(188 citation statements)

References 49 publications

Supporting

Mentioning

187

Contrasting

Order By: Relevance

“…This observation naturally raises the question of whether any existing filters in fact get close to this optimal performance [49]. Two filters that are designed to approximately achieve this goal are BM3D [48] and patchwise locally optimal Wiener (PLOW) [50], which are currently considered to be the state of the art in denoising. The BM3D algorithm can be briefly summarized by the following three steps:…”

Section: = -= --= -Tmentioning

confidence: 99%

A Tour of Modern Image Filtering: New Insights and Methods, Both Practical and Theoretical

Milanfar

2013

IEEE Signal Process. Mag.

561

351

View full text Add to dashboard Cite

Section: = -= --= -Tmentioning

confidence: 99%

A Tour of Modern Image Filtering: New Insights and Methods, Both Practical and Theoretical

Milanfar

2013

IEEE Signal Process. Mag.

561

351

View full text Add to dashboard Cite

“…An optimal spatial adaptation for patch-based image denoising method uses point-wise selection of small image patches [19]. The patch-based Wiener filter exploits patch redundancy [7]. Ghimpeteanu et al [16] describe a method in which an image decomposition technique is implemented.…”

Section: Related Workmentioning

confidence: 99%

A neighborhood regression approach for removing multiple types of noises

Sharabati

2018

J Image Video Proc.

View full text Add to dashboard Cite

Image denoising is an important first step to provide cleaned images for follow-up tasks such as image segmentation and object recognition. Many image denoising filters have been proposed, with most of the filters focusing on one particular type of additive or multiplicative noise. In this article, we propose a novel neighborhood regression approach. Using the neighboring pixels as predictors, our approach has superb performance over multiple types of noises, including Gaussian, Poisson, Gaussian and Poisson, salt & pepper, and stripped noise. Our L 2 regression filter can be parallelized to significantly speed up the denoising process to process a large number of noisy images. Meanwhile, our regression approach does not need tuning parameters or any training images, and it does not need any prior knowledge of the variance of the noise. Instead, our regression filter can accurately estimate the variance of the added Gaussian noise. We have performed extensive experiments, comparing our regression filter with the popular denoising filters, including BM3D, median filter, and wavelet filter, to demonstrate the superb performance of our proposed regression filter.

show abstract

“…Firstly, we test the performance of the proposed WSNM in image denoising, and compare it with six representative algorithms: block-matching 3D filtering [6] (BM3D), patch-based near-optimal image denoising [33] (PBNO), spatially adaptive iterative singularvalue thresholding [8] (SAIST), expected patch log likelihood for image denoising [34] (EPLL), global image denoising [35] (GID), and weighted nuclear norm minimization [27] (WNNM). It is worth to note that those methods, especially WNNM, are the schemes in the open literature whose performance has shown convincing improvements over BM3D.…”

Section: A Image Denoisingmentioning

confidence: 99%

Weighted Schatten <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math> </inline-formula>-Norm Minimization for Image Denoising and Background Subtraction

Xie

Liu

et al. 2016

IEEE Trans. on Image Process.

343

View full text Add to dashboard Cite

Abstract-Low rank matrix approximation (LRMA), which aims to recover the underlying low rank matrix from its degraded observation, has a wide range of applications in computer vision. The latest LRMA methods resort to using the nuclear norm minimization (NNM) as a convex relaxation of the nonconvex rank minimization. However, NNM tends to over-shrink the rank components and treats the different rank components equally, limiting its flexibility in practical applications. We propose a more flexible model, namely the Weighted Schatten p-Norm Minimization (WSNM), to generalize the NNM to the Schatten p-norm minimization with weights assigned to different singular values. The proposed WSNM not only gives better approximation to the original low-rank assumption, but also considers the importance of different rank components. We analyze the solution of WSNM and prove that, under certain weights permutation, WSNM can be equivalently transformed into independent non-convex lp-norm subproblems, whose global optimum can be efficiently solved by generalized iterated shrinkage algorithm. We apply WSNM to typical low-level vision problems, e.g., image denoising and background subtraction. Extensive experimental results show, both qualitatively and quantitatively, that the proposed WSNM can more effectively remove noise, and model complex and dynamic scenes compared with state-of-the-art methods.

show abstract

Patch-Based Near-Optimal Image Denoising

Cited by 268 publications

References 49 publications

A Tour of Modern Image Filtering: New Insights and Methods, Both Practical and Theoretical

A Tour of Modern Image Filtering: New Insights and Methods, Both Practical and Theoretical

A neighborhood regression approach for removing multiple types of noises

Weighted Schatten <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math> </inline-formula>-Norm Minimization for Image Denoising and Background Subtraction

Contact Info

Product

Resources

About