Results 31 to 40 of about 25,670 (189)
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
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A neural network approach for the blind deconvolution of turbulent flows
, 2017 We present a single-layer feedforward artificial neural network architecture trained through a supervised learning approach for the deconvolution of flow variables from their coarse grained computations such as those encountered in large eddy simulations.
Maulik, Romit, San, Omer
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Zernike integrated partial phase error reduction algorithm
Results in Optics, 2021 A modification to the error reduction algorithm is reported in this paper for determining the prescription of an imaging system in terms of Zernike polynomials.
Stephen C. Cain
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Blind Ptychography via Blind Deconvolution
, 2023 arXiv admin note: text overlap with arXiv:1606.04933 by other ...
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Blind Deconvolution of Seismic Data Using f-Divergences
Entropy, 2011 This paper proposes a new approach to the seismic blind deconvolution problem in the case of band-limited seismic data characterized by low dominant frequency and short data records, based on Csiszár’s f-divergence.
Bing Zhang, Jing-Huai Gao
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A Noise-Robust Method with Smoothed \ell_1/\ell_2 Regularization for Sparse Moving-Source Mapping
, 2016 The method described here performs blind deconvolution of the beamforming output in the frequency domain. To provide accurate blind deconvolution, sparsity priors are introduced with a smooth \ell_1/\ell_2 regularization term. As the mean of the noise in
Mars, Jérôme I. +3 more
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Blind and Non-Blind Deconvolution-Based Image Deblurring Techniques for Blurred and Noisy Image
Tikrit Journal of Engineering Sciences: Image deblurring is a common issue in low-level computer vision aiming to restore a clear image from a blurred input image. Deep learning innovations have significantly advanced the solution to this issue, and numerous deblurring networks have been ...
Shayma Wail Nourildean
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Guide star based deconvolution for imaging behind turbid media
Journal of the European Optical Society-Rapid Publications, 2018 Background If structures of interest are hidden beneath turbid layers such as biological tissues, imaging becomes challenging, even impossible. However, if the point spread function of the system is known from the presence of a guide star, application of
Jale Schneider, Christof M Aegerter
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Subsampled Blind Deconvolution via Nuclear Norm Minimization [PDF]
, 2020 Many phenomena can be modeled as systems that preform convolution, including negative effects on data like translation/motion blurs. Blind Deconvolution (BD) is a process used to reverse the negative effects of a system by effectively undoing the ...
Thieken, Alexander
core
Blind Deconvolution Using Modulated Inputs [PDF]
IEEE Transactions on Signal Processing, 2020 This paper considers the blind deconvolution of multiple modulated signals, and an arbitrary filter. Multiple inputs $\boldsymbol{s}_1, \boldsymbol{s}_2, \ldots, \boldsymbol{s}_N =: [\boldsymbol{s}_n]$ are modulated (pointwise multiplied) with random sign sequences $\boldsymbol{r}_1, \boldsymbol{r}_2, \ldots, \boldsymbol{r}_N =: [\boldsymbol{r}_n ...
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