Bayesian image restoration using a wavelet-based subband decomposition

Molina, Rafael; Katsaggelos, Aggelos K.; Abad, J.

doi:10.1109/icassp.1999.757536

Cited by 8 publications

(9 citation statements)

References 20 publications

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“…In all the experiments, we have considered the gradient descent iterative procedure defined in (8) [with the use of (9) and with ]. The initial estimate for the iterative restoration procedure and used as observation for the segmentation step is given by the iterative Wiener filtering.…”

Section: Resultsmentioning

confidence: 99%

“…A similar strategy reinterpreted in the wavelet-domain has been proposed by Wang et al in [7] with the following quadratic functional, , where are wavelet coefficients of at resolution level and are constants (i.e., the larger the resolution level, the larger the penalty). A similar prior model, but adaptive to the subband decomposition of , can be found in [8].…”

Section: B Existing Prior Modelsmentioning

confidence: 99%

“…In the case where several partitions into regions are considered, we can easily find (9) where is the number of pixels of the region belonging to pixel at location for the simulated partition into regions. The successive approximation of the solution according to the minimization of the cost function expressed in (6) results in initializing with the restoration result given by the Iterative Wiener filter (see Section III-A), and by using the iterative process defined in (8) [with the use of (9)] until some convergence criterion is met.…”

Section: Proposed Adaptive Edge-preserving Restoration Modelmentioning

confidence: 99%

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A segmentation-based regularization term for image deconvolution

Mignotte

2006

IEEE Trans. on Image Process.

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Abstract-This paper proposes a new and original inhomogeneous restoration (deconvolution) model under the Bayesian framework for observed images degraded by space-invariant blur and additive Gaussian noise. In this model, regularization is achieved during the iterative restoration process with a segmentation-based a priori term. This adaptive edge-preserving regularization term applies a local smoothness constraint to pre-estimated constant-valued regions of the target image. These constant-valued regions (the segmentation map) of the target image are obtained from a preliminary Wiener deconvolution estimate. In order to estimate reliable segmentation maps, we have also adopted a Bayesian Markovian framework in which the regularized segmentations are estimated in the maximum a posteriori (MAP) sense with the joint use of local Potts prior and appropriate Gaussian conditional luminance distributions. In order to make these segmentations unsupervised, these likelihood distributions are estimated in the maximum likelihood sense. To compute the MAP estimate associated to the restoration, we use a simple steepest descent procedure resulting in an efficient iterative process converging to a globally optimal restoration. The experiments reported in this paper demonstrate that the discussed method performs competitively and sometimes better than the best existing state-of-the-art methods in benchmark tests.

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Section: Resultsmentioning

confidence: 99%

Section: B Existing Prior Modelsmentioning

confidence: 99%

Section: Proposed Adaptive Edge-preserving Restoration Modelmentioning

confidence: 99%

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A segmentation-based regularization term for image deconvolution

Mignotte

2006

IEEE Trans. on Image Process.

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“…The literature on wavelet-based image restoration (see [6][7][8][9][10][11] for representative techniques) is more limited than that on wavelet image denoising (B = identity operator). Many wavelet-based image restoration schemes are direct extensions of wavelet-based denoising techniques.…”

Section: Introductionmentioning

confidence: 99%

<title>Image restoration using statistical wavelet models</title>

Liu

Moulin

2001

SPIE Proceedings

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In this paper, we propose an image restoration algorithm based on state-of-the-art wavelet domain statistical models. We present an efficient method to estimate the model parameters from the observations, and solve the restoration problem in orthonormal and translation-invariant (TI) wavelet domains. Substantial improvements over previous wavelet-based restoration methods are obtained. The use of a TI wavelet transform further enhances the restoration performance. We study the improvement from the viewpoint of Bayesian estimation theory and show that replacing an estimator with its TI version will reduce the expected risk if the signal and the degradation model are stationary.

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“…These techniques may be classified into several categories: 1) the spatial filtering methods [7]- [10]; 2) methods based on wavelet representation [11]- [14]; 3) MRF approaches [15]- [17]; and 4) the iterative regularization restoration approaches [18]- [21].…”

Section: Introductionmentioning

confidence: 99%

Blocking artifact detection and reduction in compressed data

Triantafyllidis

Tzovaras

Strintzis

2002

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-A novel frequency-domain technique for image blocking artifact detection and reduction is presented in this paper. The algorithm first detects the regions of the image which present visible blocking artifacts. This detection is performed in the frequency domain and uses the estimated relative quantization error calculated when the discrete cosine transform (DCT) coefficients are modeled by a Laplacian probability function. Then, for each block affected by blocking artifacts, its dc and ac coefficients are recalculated for artifact reduction. To achieve this, a closed-form representation of the optimal correction of the DCT coefficients is produced by minimizing a novel enhanced form of the mean squared difference of slope for every frequency separately. This correction of each DCT coefficient depends on the eight neighboring coefficients in the subband-like representation of the DCT transform and is constrained by the quantization upper and lower bound. Experimental results illustrating the performance of the proposed method are presented and evaluated.

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Bayesian image restoration using a wavelet-based subband decomposition

Cited by 8 publications

References 20 publications

A segmentation-based regularization term for image deconvolution

A segmentation-based regularization term for image deconvolution

<title>Image restoration using statistical wavelet models</title>

Blocking artifact detection and reduction in compressed data

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