Space-variant point-spread-function deconvolution of Hubble imagery using the Connection Machine

1993 ◽  
Author(s):  
Michael L. Cobb ◽  
Paul L. Hertz ◽  
Robert O. Whaley ◽  
Eric A. Hoffman
Keyword(s):  
Point Spread Function ◽  
Point Spread ◽  
Connection Machine ◽  
Spread Function ◽  
Space Variant

A Splicing Method of Sectioned Restoration Algorithm for Images with Space-Variant Point Spread Function

2009 ◽  
Vol 29 (3) ◽  
pp. 648-653 ◽  
Author(s):  
陶小平 Tao Xiaoping ◽  
冯华君 Feng Huajun ◽  
雷华 Lei Hua ◽  
李奇 Li Qi ◽  
徐之海 Xu Zhihai
Keyword(s):  
Point Spread Function ◽  
Point Spread ◽  
Restoration Algorithm ◽  
Spread Function ◽  
Space Variant

A NOVEL ANALYTICAL SPACE-VARIANT POINT SPREAD FUNCTION MODEL FOR UNDERCORRECTED OPTICAL SYSTEMS

2000 ◽  
Vol 10 (05n06) ◽  
pp. 305-313
Author(s):  
THOMAS P. COSTELLO ◽  
WASFY B. MIKHAEL
Keyword(s):  
Point Spread Function ◽  
Diffraction Theory ◽  
Optical Systems ◽  
Optical Transfer Function ◽  
Point Spread ◽  
Proposed Model ◽  
Direct Sampling ◽  
Spread Function ◽  
Optical Transfer ◽  
Space Variant

An analytical model is developed for the space-variant (SV) point-spread-function (PSF) of an undercorrected optical system with a rectangular aperture. The model accommodates broadening and shifting of the central lobe, as well as sidelobe asymmetry of the PSF, as field angle increases. These effects are exhibited by diffraction-based PSF models. The proposed model uses eight parameters for any specific field position, compared to ~ 210 parameters required for direct sampling of an individual PSF. The model is adapted to PSFs developed from diffraction theory using an adaptive system with gradient descent parameter adjustment. Consequently, the model is useful for applying certain SV digital image restoration methods because it significantly reduces the memory required to store PSF sample functions. In addition, the model does not require samples of the PSF or a DFT operation to obtain samples of the optical transfer function (OTF). Thus, the efficiency of SV restoration methods applied in the frequency domain, such as sectioning approaches, is further improved. Data presented confirms the accuracy and the computational advantage of the model by quantifying its adaptation to a physical PSF over a range of field angles.

Space variant point spread function modeling for astronomical image data processing

2007 ◽  
Author(s):  
Martin Řeřábek ◽  
Petr Páta ◽  
Karel Fliegel ◽  
Jan Švihlik ◽  
Pavel Koten
Keyword(s):  
Data Processing ◽  
Point Spread Function ◽  
Image Data ◽  
Astronomical Image ◽  
Point Spread ◽  
Spread Function ◽  
Space Variant

scholarly journals Space-variant point spread function measurement and interpolation at any depth based on single-pixel imaging

2020 ◽  
Vol 28 (7) ◽  
pp. 9244
Author(s):  
Hongzhi Jiang ◽  
Yu Wang ◽  
Xudong Li ◽  
Huijie Zhao ◽  
Yuxi Li
Keyword(s):  
Point Spread Function ◽  
Function Measurement ◽  
Point Spread ◽  
Spread Function ◽  
Single Pixel ◽  
Space Variant

scholarly journals A Space-Variant Deblur Method for Focal-Plane Microwave Imaging

2018 ◽  
Vol 8 (11) ◽  
pp. 2166
Author(s):  
Shenshen Luan ◽  
Shuguo Xie ◽  
Tianheng Wang ◽  
Xuchun Hao ◽  
Meiling Yang ◽  
...  
Keyword(s):  
Point Spread Function ◽  
Focal Plane ◽  
Imaging System ◽  
Millimetre Wave ◽  
Point Spread ◽  
Processing Step ◽  
Wide Range ◽  
Spread Function ◽  
Polar Coordinate ◽  
Space Variant

In the research of passive millimetre wave (PMMW) imaging, the focal plane array (FPA) can realize fast, wide-range imaging and detection. However, it has suffered from a limited aperture and off-axis aberration. Thus, the result of FPA is usually blurred by space-variant point spread function (SVPSF) and is hard to restore. In this paper, a polar-coordinate point spread function (PCPSF) model is presented to describe the circle symmetric characteristic of space-variant blur, and a log-polar-coordinate transformation (LPCT) method is propagated as the pre-processing step before the Lucy–Richardson algorithm to eliminate the space variance of blur. Compared with the traditional image deblur method, LPCT solves the problem by analyzing the physical model instead of the approximating it, which has proved to be a feasible way to deblur the FPA imaging system.

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