Multichannel Blind Deconvolution-Based on-Orbit Estimation of Point Spread Function for Space Optical Remote Sensors

2013 ◽  
Vol 33 (4) ◽  
pp. 0428001 ◽  
Author(s):  
郭玲玲 Guo Lingling ◽  
吴泽鹏 Wu Zepeng ◽  
张立国 Zhang Liguo ◽  
任建岳 Ren Jianyue
Keyword(s):  
Point Spread Function ◽  
Blind Deconvolution ◽  
Remote Sensors ◽  
Point Spread ◽  
Spread Function ◽  
Orbit Estimation

scholarly journals Determination of the point spread function of layered metamaterials assisted with the blind deconvolution algorithm

2014 ◽  
Vol 47 (1) ◽  
pp. 17-26 ◽  
Author(s):  
David Pastor ◽  
Tomasz Stefaniuk ◽  
Piotr Wróbel ◽  
Carlos J. Zapata-Rodríguez ◽  
Rafał Kotyński
Keyword(s):  
Point Spread Function ◽  
Blind Deconvolution ◽  
Point Spread ◽  
Deconvolution Algorithm ◽  
Spread Function

PERFORMANCE OF BLIND DECONVOLUTION IN OPTOACOUSTIC TOMOGRAPHY

2011 ◽  
Vol 04 (04) ◽  
pp. 385-393 ◽  
Author(s):  
THOMAS JETZFELLNER ◽  
VASILIS NTZIACHRISTOS
Keyword(s):  
Point Spread Function ◽  
Near Infrared ◽  
Blind Deconvolution ◽  
Photoacoustic Imaging ◽  
Reconstructed Image ◽  
Optoacoustic Tomography ◽  
Point Spread ◽  
Spread Function ◽  
Old Mice ◽  
Unknown Point

In this paper, we consider the use of blind deconvolution for optoacoustic (photoacoustic) imaging and investigate the performance of the method as means for increasing the resolution of the reconstructed image beyond the physical restrictions of the system. The method is demonstrated with optoacoustic measurement obtained from six-day-old mice, imaged in the near-infrared using a broadband hydrophone in a circular scanning configuration. We find that estimates of the unknown point spread function, achieved by blind deconvolution, improve the resolution and contrast in the images and show promise for enhancing optoacoustic images.

scholarly journals Blurred Image Restoration with Unknown Point Spread Function

2018 ◽  
Vol 29 (1) ◽  
pp. 189 ◽  
Author(s):  
Ghada Sabah Karam
Keyword(s):  
Image Quality ◽  
Point Spread Function ◽  
Image Quality Assessment ◽  
Blind Deconvolution ◽  
Assessment Method ◽  
Reference Image ◽  
Point Spread ◽  
Spread Function ◽  
Quality Assessment Method ◽  
Blurred Image

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. We proposed adaptive blind- non reference image quality assessment method for estimation the blur function (i.e. point spread function PSF) from the image acquired under low-lighting conditions and defocus images using Bayesian Blind Deconvolution. It is based on predicting a sharp version of a blurry inter image and uses the two images to solve a PSF. The estimation down by trial and error experimentation, until an acceptable restored image quality is obtained. Assessments the qualities of images have done through the applications of a set of quality metrics. Our method is fast and produces accurate results.

scholarly journals Application of Blind Deconvolution Algorithm for Deblurring of Saturated Images

2019 ◽  
Vol 8 (2S3) ◽  
pp. 1383-1386
Keyword(s):  
Image Processing ◽  
Image Restoration ◽  
Point Spread Function ◽  
Blind Deconvolution ◽  
Visual Quality ◽  
Visual Examination ◽  
Point Spread ◽  
Spread Function ◽  
Sharp Image ◽  
Deblurring And Denoising

Image Restoration is a field of Image Processing which manages recuperating a unique and sharp image from a debased image utilizing a numerical corruption and reclamation model. This investigation centers around rebuilding of corrupted images which have been obscured by known or obscure debasement work. Image rebuilding which reestablishes an unmistakable image from a solitary haze image is a troublesome issue of assessing two questions: a point spread function (PSF) and its optimal image. Image deblurring can improve visual quality and mitigates movement obscure for dynamic visual examination. We propose a strategy to deblur immersed images for dynamic visual examination by applying obscure piece estimation and deconvolution demonstrating. The haze portion is assessed in a change space, though the deconvolution model is decoupled into deblurring and denoising stages by means of variable part

Three-dimensional image reconstruction of fluorescence micrographs without knowing the point spread function

1993 ◽  
Vol 51 ◽  
pp. 152-153
Author(s):  
Timothy J. Holmes ◽  
Vijaykumar Krishnamurthi ◽  
Yi-Hwa Liu
Keyword(s):  
Point Spread Function ◽  
Explicit Knowledge ◽  
Blind Deconvolution ◽  
Fluorescence Probe ◽  
Three Dimensional ◽  
Mathematical Optimization ◽  
Likelihood Estimation ◽  
Optimization Approach ◽  
Point Spread ◽  
Spread Function

The term blind deconvolution refers to the deconvolution, or deblurring, of a signal (optical, sound, or other) without explicit knowledge of the point spread function (PSF). The PSF is, instead, reconstructed concurrently with the deblurred signal from the collected, noisy data. Blind deconvolution methodologies have been under study for general signal processing applications, such as in restoring phonographs, as early as 1968. Ayers and Dainty have performed pioneering blind deconvolution research for deblurring 2D images, which has been applied to astronomy. Inspired by this earlier blind deconvolution research, we have taken a new approach which is based on maximum likelihood estimation (MLE). Our approach is extended from previous research in applying MLE to positron-emission tomography (PET). A novelty of our research is in the application of MLE and blind deconvolution to 2D and 3D fluorescence microscopy.The fundamental advantage of the MLE approach over some of the other blind deconvolution approaches is that it is a mathematical optimization approach, wherein the likelihood functional of the collected image data, the fluorescence probe concentration and the PSF is maximized.

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