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Understanding Blind Deconvolution Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011Blind deconvolution is the recovery of a sharp version of a blurred image when the blur kernel is unknown. Recent algorithms have afforded dramatic progress, yet many aspects of the problem remain challenging and hard to understand. The goal of this paper is to analyze and evaluate recent blind deconvolution algorithms both theoretically and ...
Anat, Levin +3 more
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Compact multiframe blind deconvolution
Optics Letters, 2011We describe a multiframe blind deconvolution (MFBD) algorithm that uses spectral ratios (the ratio of the Fourier spectra of two data frames) to model the inherent temporal signatures encoded by the observed images. In addition, by focusing on the separation of the object spectrum and system transfer functions only at spatial frequencies where the ...
Douglas A, Hope, Stuart M, Jefferies
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Total variation blind deconvolution
IEEE Transactions on Image Processing, 1998In this paper, we present a blind deconvolution algorithm based on the total variational (TV) minimization method proposed. The motivation for regularizing with the TV norm is that it is extremely effective for recovering edges of images as well as some blurring functions, e.g., motion blur and out-of-focus blur.
Chan, Tony F., Wong, Chiukwong
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SIAM Journal on Applied Mathematics, 2001
Summary: Blind deconvolution seeks to deblur an image without knowing the cause of the blur. Iterative methods are commonly applied to that problem, but the iterative process is slow, uncertain, and often ill-behaved. This paper considers a significant but limited class of blurs that can be expressed as convolutions of two-dimensional symmetric Lévy ...
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Summary: Blind deconvolution seeks to deblur an image without knowing the cause of the blur. Iterative methods are commonly applied to that problem, but the iterative process is slow, uncertain, and often ill-behaved. This paper considers a significant but limited class of blurs that can be expressed as convolutions of two-dimensional symmetric Lévy ...
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Regularized blind deconvolution
Signal Recovery and Synthesis, 1998Blind deconvolution is an important problem that arises in many fields of research. It is of particular relevance to imaging through turbulence where the point spread function can only be modelled statistically, and direct measurement may be difficult. We describe this problem by a noisy convolution, where f(x, y) represents the true image, h(x, y) the
R.G. Lane +3 more
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IEEE Signal Processing Magazine, 1996
The goal of image restoration is to reconstruct the original scene from a degraded observation. This recovery process is critical to many image processing applications. Although classical linear image restoration has been thoroughly studied, the more difficult problem of blind image restoration has numerous research possibilities.
D. Kundur, D. Hatzinakos
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The goal of image restoration is to reconstruct the original scene from a degraded observation. This recovery process is critical to many image processing applications. Although classical linear image restoration has been thoroughly studied, the more difficult problem of blind image restoration has numerous research possibilities.
D. Kundur, D. Hatzinakos
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Blind deconvolution: multiplicative iterative algorithm
Optics Letters, 2007A new algorithm has been developed for performing blind deconvolution on degraded images. The algorithm naturally preserves the nonnegative constraint on the iterative solutions of blind deconvolution and can produce a restored image of high resolution.
Jianlin, Zhang +2 more
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Kuicnet Algorithms for Blind Deconvolution
Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378), 1998We show how the recently-developed KuicNet method for instantaneous blind source separation can be extended to the blind deconvolution task. The proposed algorithm has a simple form and is effective in deconvolving source signals with non-zero kurtoses from a linear filtered version of the source sequence.
S.C. Douglas, S.-Y. Kung
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