Results 31 to 40 of about 323,867 (242)

The Residual ISI for Which the Convolutional Noise Probability Density Function Associated with the Blind Adaptive Deconvolution Problem Turns Approximately Gaussian

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

Convex Sparse Blind Deconvolution

CoRR, 2021
In the blind deconvolution problem, we observe the convolution of an unknown filter and unknown signal and attempt to reconstruct the filter and signal. The problem seems impossible in general, since there are seemingly many more unknowns than knowns .
Qingyun Sun, David L. Donoho
openaire   +3 more sources

Restoration of Out-of-Focus Fluorescence Microscopy Images Using Learning-Based Depth-Variant Deconvolution

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, De Cai, Jiasheng Zhou, Jiajia Luo, Sung-Liang Chen   +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, Duan Baojun, Yan Weipeng, Song Yan, Han Changcai, Ma Jiming, Song Guzhou   +6 more
doaj   +1 more source

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

A Clearer Picture of Total Variation Blind Deconvolution [PDF]

, 2015
Blind deconvolution is the problem of recovering a sharp image and a blur kernel from a noisy blurry image. Recently, there has been a significant effort on understanding the basic mechanisms to solve blind deconvolution.
Favaro, Paolo, Perrone, Daniele
core   +3 more sources

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
doaj   +1 more source

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
doaj   +1 more source

Blind atmospheric turbulence deconvolution [PDF]

IET Image Processing, 2020
A new blind image deconvolution technique is developed for atmospheric turbulence deblurring to overcome limitations of ‘generic’ blind deconvolution algorithms that do not take into account the complicated physics of the turbulence. The originality of the proposed approach relies on an actual physical model, known as the Fried kernel, that quantifies ...
Charles-Alban Deledalle, Jérôme Gilles
openaire   +1 more source

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
doaj   +1 more source