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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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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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Normalized Blind Deconvolution
2018We introduce a family of novel approaches to single-image blind deconvolution, i.e., the problem of recovering a sharp image and a blur kernel from a single blurry input. This problem is highly ill-posed, because infinite (image, blur) pairs produce the same blurry image.
Meiguang Jin, Stefan Roth, Paolo Favaro
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Blind Deconvolution With Model Discrepancies
IEEE Transactions on Image Processing, 2017Blind deconvolution is a strongly ill-posed problem comprising of simultaneous blur and image estimation. Recent advances in prior modeling and/or inference methodology led to methods that started to perform reasonably well in real cases. However, as we show here, they tend to fail if the convolution model is violated even in a small part of the image.
Jan Kotera, Vaclav Smidl, Filip Sroubek
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Near optimal blind deconvolution
ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing, 2003A solution is proposed for blind deconvolution problems, i.e. the estimation of the impulse response of an unknown discrete-time channel given the output data sequence and statistical information on the input sequence. The solution approaches the optimal one when the input sequence is independent and identically distributed and the channel distortion ...
S. Bellini, F. Rocca
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Deconvolution and Blind Deconvolution in Astronomy
2017Fionn Murtagh Dept. Computer Science, Royal Holloway, University of London, Egham, UK e-mail: fmurtagh@acm.orgThis chapter reviews different astronomical deconvolution methods. The all-pervasive presence of noise is what makes deconvolution particularly difficult.
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