Results 21 to 30 of about 28,366 (229)
IEEE Transactions on Signal Processing, 2019 We introduce a novel multichannel blind deconvolution (BD) method that extracts sparse and front-loaded impulse responses from the channel outputs, i.e., their convolutions with a single arbitrary source. A crucial feature of this formulation is that it doesn't encode support restrictions on the unknowns, unlike most prior work on BD. The indeterminacy
Pawan Bharadwaj +2 more
openaire +3 more sources
Phase and TV Based Convex Sets for Blind Deconvolution of Microscopic Images [PDF]
, 2015 In this article, two closed and convex sets for blind deconvolution problem are proposed. Most blurring functions in microscopy are symmetric with respect to the origin.
Cetin, A. Enis +2 more
core +1 more source
Semi-blind Sparse Image Reconstruction with Application to MRFM [PDF]
, 2012 We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known.
Dobigeon, Nicolas +2 more
core +4 more sources
New Lagrange Multipliers for the Blind Adaptive Deconvolution Problem Applicable for the Noisy Case
Entropy, 2016 Recently, a new blind adaptive deconvolution algorithm was proposed based on a new closed-form approximated expression for the conditional expectation (the expectation of the source input given the equalized or deconvolutional output) where the output ...
Monika Pinchas
doaj +1 more source
Blind deconvolution of sparse pulse sequences under a minimum distance constraint: a partially collapsed Gibbs sampler method [PDF]
, 2012 For blind deconvolution of an unknown sparse sequence convolved with an unknown pulse, a powerful Bayesian method employs the Gibbs sampler in combination with a Bernoulli–Gaussian prior modeling sparsity.
Dobigeon, Nicolas +3 more
core +3 more sources
Entropy, 2022 In a blind adaptive deconvolution problem, the convolutional noise observed at the output of the deconvolution process, in addition to the required source signal, is—according to the literature—assumed to be a Gaussian process when the deconvolution ...
Monika Pinchas
doaj +1 more source
IEEE Photonics Journal, 2020 Image quality is degraded in the out-of-focus region because of the depth-variant (DV) point spread function (DV-PSF) of a fluorescence microscope. Either non-blind or blind deconvolution for restoration results in limited improvement.
Da He +4 more
doaj +1 more source
Linear Reconstruction Methods for Large Thick Aperture Imaging
MATEC Web of Conferences, 2018 Large thick aperture imaging method is proposed to measure the radiation intensity distribution of radiation source whose size is several centimetres. The new method contains two steps which are coded imaging and image reconstruction.
Yao Zhiming +6 more
doaj +1 more source
Blind Deconvolution Using Convex Programming [PDF]
IEEE Transactions on Information Theory, 2014 40 pages, 8 ...
Ahmed, Ali +2 more
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Blurred Image Restoration with Unknown Point Spread Function
Al-Mustansiriyah Journal of Science, 2018 Blurring image caused by a number of factors such as de focus, motion, and limited sensor resolution. Most of existing blind deconvolution research concentrates at recovering a single blurring kernel for the entire image.
ghada sabah karam
doaj +1 more source