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Handling noise in image deconvolution with local/non-local priors
2014
2014 IEEE International Conference on Image Processing (ICIP)
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Making use of natural sparse priors has shown to reduce ringing artifacts but handling noise remains limited. ...
On the other hand, non-local priors have shown to give the best results in image denoising. We propose in this paper to combine both local and non-local priors to handle noise. ...
This prior is usually considered on the derivatives and modeled using a hyper-Laplacian law [7, 8] . ...
doi:10.1109/icip.2014.7025535
dblp:conf/icip/BadriY14
fatcat:yzpv2aqkjvdwhb4shxwxwwvwsu
Image denoising via group sparsity residual constraint
2017
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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To enhance the performance of group sparse-based image denoising, the concept of group sparsity residual is proposed, and thus, the problem of image denoising is translated into one that reduces the group ...
In this paper, we propose a new prior model for image denoising via group sparsity residual constraint (GSRC). ...
is among the most remarkable priors for image restoration. ...
doi:10.1109/icassp.2017.7952464
dblp:conf/icassp/ZhaLZHSSTBWZ17
fatcat:q6esycn6lza35icn45yk3t3ipe
Group Sparsity Residual Constraint for Image Denoising
[article]
2017
arXiv
pre-print
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Unlike the conventional group-based sparse representation denoising methods, two kinds of prior, namely, the NSS priors of noisy and pre-filtered images, are used in GSRC. ...
In particular, we integrate these two NSS priors through the mechanism of sparsity residual, and thus, the task of image denoising is converted to the problem of reducing the group sparsity residual. ...
The distribution of the group sparsity residual R for image lena with according to Eq. (7), one can observe that these two NSS
σ=30 and fitting Gaussian, Laplacian and hyper-Laplacian ...
arXiv:1703.00297v6
fatcat:obxufquw6jajxlqmrqvsz4cf2e
Texture Enhanced Image Denoising via Gradient Histogram Preservation
2013
2013 IEEE Conference on Computer Vision and Pattern Recognition
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Various natural image priors, such as gradient based prior, nonlocal self-similarity prior, and sparsity prior, have been extensively exploited for noise removal. ...
The denoising algorithms based on these priors, however, tend to smooth the detailed image textures, degrading the image visual quality. ...
Acknowledgements This work is supported by NSFC under Grant No. 61271093, the Hong Kong Scholar Program, and the program of ministry of education for new century excellent talents. ...
doi:10.1109/cvpr.2013.159
dblp:conf/cvpr/ZuoZSZ13
fatcat:q3cxptmfqfhsrfsrhlc4s3o2ou
Image denoising via group sparsity residual constraint
[article]
2017
arXiv
pre-print
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To enhance the performance of group sparse-based image denoising, the concept of group sparsity residual is proposed, and thus, the problem of image denoising is translated into one that reduces the group ...
In this paper, we propose a new prior model for image denoising via group sparsity residual constraint (GSRC). ...
is among the most remarkable priors for image restoration. ...
arXiv:1609.03302v5
fatcat:sb6yt3yv4zakjmppr4dkpw7ngu
Four-Directional Total Variation With Overlapping Group Sparsity for Image Denosing
2021
IEEE Access
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[19] adopted hyper-Laplacian prior with overlapping group sparsity for image restoration. The new model achieves a good balance between preserving features and overcoming staircase effects. ...
with overlapping group sparsity (OGSTV4) for image denoising. ...
He has taken charge of and accomplished over 10 projects supported by the National Natural Science Foundation, and so on. ...
doi:10.1109/access.2021.3058120
fatcat:cew32tverncztjoe2yu5mluup4
Nonlocal Hierarchical Dictionary Learning using Wavelets and Gradient Histogram Preservation for Image Denoising: A Review
2015
International Journal of Computer Applications
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One common approach is to use a Gaussian filter, In spite of the great success of many denoising algorithms; they tend to smooth the fine scale image textures when removing noise, degrading the image visual ...
The Nonlocal Hierarchical Dictionary Learning using Wavelet (NHDLW) and Gradient Histogram Preservation (GHP),which is large success in denoising. ...
C is the normalization factor γ and k are the two parameters of the hyper-Laplacian distribution. ...
doi:10.5120/ijca2015907516
fatcat:o7cz2l6ta5hhzlp5oh3gkwdxoi
Exploiting Image Local and Nonlocal Consistency for Mixed Gaussian-Impulse Noise Removal
2012
2012 IEEE International Conference on Multimedia and Expo
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2012 pre-print arXiv:1208.3718v1 fatcat:ie3hhthl6bbi5noq34ztcja5bq
Specifically, the local consistency is measured by a hyper-Laplace prior, enforcing the local smoothness of images, while the nonlocal consistency is measured by three-dimensional sparsity of similar blocks ...
Most existing image denoising algorithms can only deal with a single type of noise, which violates the fact that the noisy observed images in practice are often suffered from more than one type of noise ...
In this paper, we measure the image local consistency by hyper-Laplacian priors, defined by 2 3 ( ) LC ∇ = Φ x x , (4) where ∇ represents the spatial gradients of the image in both vertical and horizontal ...
doi:10.1109/icme.2012.109
dblp:conf/icmcs/ZhangXZMZ12
fatcat:7m2rvamx3fcl5pr3bdcxbpr63u
A Simple Local Minimal Intensity Prior and An Improved Algorithm for Blind Image Deblurring
[article]
2020
arXiv
pre-print
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The PMP of clear images is much more sparse than that of blurred ones, and hence is very effective in discriminating between clear and blurred images. ...
The new algorithm flexibly imposes sparsity inducing on the PMP under the MAP framework rather than directly uses the half quadratic splitting algorithm. ...
The gradient sparsity prior of natural images is the most widely used prior. ...
arXiv:1906.06642v4
fatcat:lotuowljyfeuxlx7hyxwxu4xla
Compressed Sensing Recovery via Collaborative Sparsity
2012
2012 Data Compression Conference
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Experimental results on a wide range of natural images are presented to demonstrate the efficacy of the new CS recovery strategy. ...
DCT, wavelet and gradient domain) for the entirety of a signal, which are irrespective of the nonstationarity of natural signals and cannot achieve high enough degree of sparsity, thus resulting in poor ...
Acknowledgement We would like to thank the authors of [7] and [8] for kindly providing their codes. This work is supported in part by National Science Foundation ...
doi:10.1109/dcc.2012.71
dblp:conf/dcc/ZhangZZXMG12
fatcat:h3rlmfh3yffd5im7k4gyoy7hha
A Non-Local Low-Rank Approach to Enforce Integrability
2016
IEEE Transactions on Image Processing
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, 2) the low-rank prior efficiently reduces dense noise as it has been shown in recent image restoration works. ...
Our formulation consists in a sparse gradient data-fitting term to handle outliers together with a gradient-domain non-local low-rank prior. ...
For the non-local sparsity part, as processing each group is independent (because the groups are non-overlapping), the low-rank processing step can be performed in parallel as well. ...
doi:10.1109/tip.2016.2570548
pmid:27214898
fatcat:lukc4zdqhjdczftjygegh6xd2y
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding
[article]
2020
arXiv
pre-print
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The priors used in this presentation include variants of total variation, Laplacian regularization, bilateral filtering, sparse coding on learned dictionaries, and non-local self similarities. ...
This approach is appealing for solving imaging problems, as it allows the use of classical image priors within deep models that are trainable end to end. ...
Algorithm 1 Pseudocode of the general training procedure for image restoration 1: Sample a minibatch of pairs of corrupted/clean images {(x 0 , y 0 ), · · · , (x K , y K )}; 2: Extract overlapping patches ...
arXiv:2006.14859v2
fatcat:nj7puapunnbu5eqenovg2sgitm
Infrared Image Deblurring via High-Order Total Variation and Lp-Pseudonorm Shrinkage
2020
Applied Sciences
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Second, it employs the L1-norm to describe the sparsity of image gradients, while the L1-norm has a limited capacity of depicting the sparsity of sparse variables. ...
First, the conventional ATV regularization just considers the sparsity of the first-order image gradients, thus leading to staircase artifacts. ...
Acknowledgments: Thanks to X.L. of the University of Electronic Science and Technology of China for sharing the FTV4Lp code. Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app10072533
fatcat:lsca5gwsobc2rid4y3z26dftta
Learning Discriminative Data Fitting Functions for Blind Image Deblurring
2017
2017 IEEE International Conference on Computer Vision (ICCV)
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While existing algorithms mainly focus on developing image priors for blur kernel estimation and non-blind deconvolution, only a few methods consider the effect of data fitting functions. ...
Extensive experiments on challenging motion blurred images demonstrate the proposed algorithm performs favorably against the state-of-the-art methods. ...
In addition to image priors, another group of methods focus on sharp edge predictions for blur kernel estimation. ...
doi:10.1109/iccv.2017.122
dblp:conf/iccv/PanDTS017
fatcat:hkksxv6ifnb6fkgi46icomggim
Nonconvex Weighted $\ell _p$ Minimization Based Group Sparse Representation Framework for Image Denoising
2017
IEEE Signal Processing Letters
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2017 pre-print arXiv:1704.01429v2 fatcat:wrowo6qr2nbvnmox5hossw3p6qOther Versions
Nonlocal image representation or group sparsity has attracted considerable interest in various low-level vision tasks and has led to several state-of-the-art image denoising techniques, such as BM3D, LSSC ...
However, using convex regularization can not still obtain the correct sparsity solution under some practical problems including image inverse problems. ...
These methods actually assume that natural image gradients exhibit heavy-tailed distributions, which can be fitted by Laplacian or hyper-Laplacian models [11] . ...
doi:10.1109/lsp.2017.2731791
fatcat:uhtz3e42yvh77g67bv4wme3phi
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