A Dictionary Learning Approach for Poisson Image Deblurring

Ma, Lingling; Moisan, Lionel; Yu, Jian; Zeng, Tieyong

doi:10.1109/tmi.2013.2255883

Cited by 87 publications

(10 citation statements)

References 66 publications

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“…The K-SVD is the iterative process in which two consecutive steps take place sparse coding of the examples using the current dictionary and updating the dictionary atoms for optimum data fitting. Some other works as in [33], [34] follow the same workflow like that of K-SVD with variation in dictionary and optimization problem. The clustering-based sparse representation involves a cost function (double header 1 optimization problem) in which both structural structuring and dictionary learning is used as the regularizer.…”

Section: ) Sparsity-based Dictionary Learning Modelsmentioning

confidence: 99%

Image De-Noising With Machine Learning: A Review

et al. 2021

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Images are susceptible to various kinds of noises, which corrupt the pictorial information stored in the images. Image de-noising has become an integral part of the image processing workflow. It is used to attenuate the noises and accentuate the specific image information stored within. Machine learning is an important tool in the image-de-noising workflow in terms of its robustness, accuracy, and time requirement. This paper explores the numerous state-of-the-art machine-learning-based image de-noisers like dictionary learning models, convolutional neural networks and generative adversarial networks for a range of noises like Gaussian, Impulse, Poisson, Mixed and Real-World noises. The motivation, algorithm and framework of different machine learning de-noisers are analyzed. These de-noisers are compared using PSNR as quality assessment metric on some benchmark datasets. The best de-noising results for different noise type is discussed along with future prospects. Among various Gaussian noise de-noisers, GCBD, BRDNet and PReLU network prove to be promising. CNN+LSTM, and MC2RNet are most suitable CNNbased Poisson de-noisers. For impulse noise removal, Blind CNN, and CNN+PSO perform well. For mixed noise removal, WDL, EM-CNN, CNN, SDL, and Mixed CNN are prominent. De-noisers like GRDN and DDFN show accurate results in the domain of real-world de-noising.

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Section: ) Sparsity-based Dictionary Learning Modelsmentioning

confidence: 99%

Image De-Noising With Machine Learning: A Review

et al. 2021

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“…Sparse representation 1 – 5 is an important technique for pattern classification. The sparse representation-based methods 6 – 12 use a small number of atoms from a redundant dictionary to reconstruct a given signal. It is a critical issue that adaptively learns a dictionary with powerful representation and discrimination capabilities from training samples.…”

Section: Introductionmentioning

confidence: 99%

Joint structured constraint discriminant analysis dictionary learning for pattern classification

Zhang

et al. 2023

J. Electron. Imag.

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.Analysis dictionary learning (DL) has been successfully applied to the field of pattern classification. However, it is still a challenge to utilize the local structure information and the class information of samples to improve the discrimination capability of analysis dictionary. We proposed a joint structured constraint discriminant analysis DL (ADL) method (JSCDADL) to learn a structured discriminant analysis dictionary by combining the local structure information and the structured information of samples. Specifically, we first designed an adaptive local structure preserving term (ALSPT) to improve the discrimination capability of analysis dictionary. It adaptively transmits the local structure information of samples to analysis dictionary, which ensures that the same class of samples has similar sparse codes under the action of the analysis dictionary. Then, we designed a discriminative sparse coding error term that forces the coding coefficient matrix to have the desired block diagonal structure. To further enhance the discrimination capability of analysis dictionary, we designed an analysis dictionary combination term by constantly approximating the two analysis dictionaries learned to obtain an analysis dictionary with the local structure information and the structured information of samples. Moreover, we designed an effective iterative algorithm to solve the optimization problem of JSCDADL. Extensive experimental results on six datasets demonstrate that JSCDADL can achieve satisfactory classification performance compared with some state-of-the-art methods.

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“…Zhang et al [33] combined the fractional-order TV with non-local TV to alleviate the staircase artifacts for the cartoon component as well as to preserve the details for the texture component. Ma et al [34] proposed a hybrid regularizer containing a patch-based sparsity promoting prior over a learned dictionary and a pixel-based total variation prior, but it requires additional strategies to reduce the high computational cost.…”

Section: Introductionmentioning

confidence: 99%

Poisson noisy image restoration via overlapping group sparse and nonconvex second-order total variation priors

Jon

et al. 2021

PLoS ONE

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The restoration of the Poisson noisy images is an essential task in many imaging applications due to the uncertainty of the number of discrete particles incident on the image sensor. In this paper, we consider utilizing a hybrid regularizer for Poisson noisy image restoration. The proposed regularizer, which combines the overlapping group sparse (OGS) total variation with the high-order nonconvex total variation, can alleviate the staircase artifacts while preserving the original sharp edges. We use the framework of the alternating direction method of multipliers to design an efficient minimization algorithm for the proposed model. Since the objective function is the sum of the non-quadratic log-likelihood and nonconvex nondifferentiable regularizer, we propose to solve the intractable subproblems by the majorization-minimization (MM) method and the iteratively reweighted least squares (IRLS) algorithm, respectively. Numerical experiments show the efficiency of the proposed method for Poissonian image restoration including denoising and deblurring.

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A Dictionary Learning Approach for Poisson Image Deblurring

Cited by 87 publications

References 66 publications

Image De-Noising With Machine Learning: A Review

Image De-Noising With Machine Learning: A Review

Joint structured constraint discriminant analysis dictionary learning for pattern classification

Poisson noisy image restoration via overlapping group sparse and nonconvex second-order total variation priors

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