An EM-based hybrid Fourier-wavelet image deconvolution algorithm

Hanif, Muhammad; Seghouane, Abd‐Krim

doi:10.1109/icip.2013.6738122

Cited by 2 publications

(3 citation statements)

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“…Image restoration is a fundamental and important problem in the field of image processing. The image restoration problem is often represented by a linear model as [1] where H is a linear blurring operator, and n1 represents the Gaussian white noise with variance 2 . Our goal is to obtain the original image u from the blurred image g. The problem of finding u from (1) is a discrete linear inverse problem.…”

Section: Introductionmentioning

confidence: 99%

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Parameter selection and solution algorithm for TGV-based image restoration model

2019

SN Appl. Sci.

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In this paper, image restoration problem is formulated to solve a total generalized variation (TGV)-based minimization problem. The minimization problem includes an unknown regularization parameter. A Morozov's discrepancy principlebased method is used to choose a suitable regularization parameter. Computationally, by introducing two dual variables, the TGV-based image restoration problem is reformulated as a convex-concave saddle-point problem. Meanwhile, the Chambolle-Pock's first-order primal-dual algorithm is transformed into a different equivalent form which can be seen as a proximal-based primal-dual algorithm. Then, the different equivalent form is used to solve the saddle-point problem. At last, compared with several existing state-of-the-art methods, experimental results demonstrate the performance of our proposed method.

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Section: Introductionmentioning

confidence: 99%

“…Experimental results in [3] show that the TGV-based image restoration model is effective in eliminating the staircase effect. Bredies et al [3] proposed a TGV-based image restoration model which can be written as (1)…”

Section: Introductionmentioning

confidence: 99%

Parameter selection and solution algorithm for TGV-based image restoration model

2019

SN Appl. Sci.

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“…These methods can be categorized into two groups [1]; i) disjoint blur identification and image restoration; ii) estimate blur and sharp image simultaneously in one procedure. The deconvolution process is mostly ill posed and very sensitive to the noise that requires a regularized and constrained approach for a plausible estimate of the underlying sharp image [7]. Recently, sparsity based image deblurring methods have shown significant improvement [8,9].…”

Section: Introductionmentioning

confidence: 99%

Blind image deblurring using non-negative sparse approximation

Hanif

Seghouane

2014

2014 IEEE International Conference on Image Processing (ICIP)

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Blurring is a common source of image degradation in many applications. Blind image deblurring (BID) is an apposite approach for blur removal in real images. Being an ill-posed linear inverse problem, a regularized and well constrained approach is required for a credible solution of BID model. Recently sparse representation base modeling emerged as an efficacious tool in image processing community, with application as regularizer in inverse problems. In this work the sparsity constraint is fused with the non-negative matrix approximation to address the BID problem. An alternative-iterative frame work is developed to estimate the non-negative sparse approximation of the sharp image and blurring kernel. With sparsity constraint, an estimate of the sharp image is obtained without solving the ill-posed deconvolution model. Although similar formulation has been proposed but unlike other BID methods the proposed approach is parameter free and requires no prior statistics. The experimental results validate comparatively better performance of proposed method against the other methods.

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An EM-based hybrid Fourier-wavelet image deconvolution algorithm

Cited by 2 publications

References 25 publications

Parameter selection and solution algorithm for TGV-based image restoration model

Parameter selection and solution algorithm for TGV-based image restoration model

Blind image deblurring using non-negative sparse approximation

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