Blur identification from vector quantizer encoder distortion

Panchapakesan, K.; Sheppard, D.G.; Marcellin, Michael W.; Hunt, B. R.

doi:10.1109/icip.1998.999058

Cited by 4 publications

(11 citation statements)

References 6 publications

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“…Another class of methods for regularization of BD algorithms are learning based algorithms. Not much research has been conducted in this field, and we note the recent work in [45], [47], [39]. Similar methods are also used for example-based super-resolution algorithms [23], [15].…”

Section: Previous Workmentioning

confidence: 99%

Blind Image Deconvolution Using Machine Learning for Three-Dimensional Microscopy

Kenig

Kam

Feuer

2010

IEEE Trans. Pattern Anal. Mach. Intell.

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In this work, we propose a novel method for the regularization of blind deconvolution algorithms. The proposed method employs example-based machine learning techniques for modeling the space of point spread functions. During an iterative blind deconvolution process, a prior term attracts the point spread function estimates to the learned point spread function space. We demonstrate the usage of this regularizer within a Bayesian blind deconvolution framework and also integrate into the latter a method for noise reduction, thus creating a complete blind deconvolution method. The application of the proposed algorithm is demonstrated on synthetic and real-world three-dimensional images acquired by a wide-field fluorescence microscope, where the need for blind deconvolution algorithms is indispensable, yielding excellent results.

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Section: Previous Workmentioning

confidence: 99%

Blind Image Deconvolution Using Machine Learning for Three-Dimensional Microscopy

Kenig

Kam

Feuer

2010

IEEE Trans. Pattern Anal. Mach. Intell.

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“…If access to multiple instances of the same image blurred by substantially different PSFs is available, [8] can be used in either direct or indirect configuration. An alternative approach to direct identification uses vector quantisation to train a classification system to recognise various types of PSF, but requires the system be trained for specific images [18]. A direct method that allows identification of complicated, nonlinear motion PSF paths is [1], which uses special hardware to achieve high spatial and temporal resolution.…”

Section: Blur Estimationmentioning

confidence: 99%

Robust defocus blur identification in the context of blind image quality assessment

Marais

Steyn

2007

Signal Processing: Image Communication

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“…In some practical situations, the motion blur may not be a linear motion vector in a single direction. To identify more complex models of blurs, several methods such as auto-regressive (AR) model and maximum likelihood (ML) based methods [7], [8], vector quantization (VQ) based methods [9], [10] and multiple-image-based methods [11], [12], are proposed. Although these methods can be employed for more complex motion models, there are some practical limitations.…”

Section: Introductionmentioning

confidence: 99%

Identification of Piecewise Linear Uniform Motion Blur

Patanukhom

Nishihara

2008

IEICE Transactions on Fundamentals of Electronics, Communicatio

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A motion blur identification scheme is proposed for nonlinear uniform motion blurs approximated by piecewise linear models which consist of more than one linear motion component. The proposed scheme includes three modules that are a motion direction estimator, a motion length estimator and a motion combination selector. In order to identify the motion directions, the proposed scheme is based on a trial restoration by using directional forward ramp motion blurs along different directions and an analysis of directional information via frequency domain by using a Radon transform. Autocorrelation functions of image derivatives along several directions are employed for estimation of the motion lengths. A proper motion combination is identified by analyzing local autocorrelation functions of non-flat component of trial restored results. Experimental examples of simulated and real world blurred images are given to demonstrate a promising performance of the proposed scheme.

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Blur identification from vector quantizer encoder distortion

Cited by 4 publications

References 6 publications

Blind Image Deconvolution Using Machine Learning for Three-Dimensional Microscopy

Blind Image Deconvolution Using Machine Learning for Three-Dimensional Microscopy

Robust defocus blur identification in the context of blind image quality assessment

Identification of Piecewise Linear Uniform Motion Blur

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