Imaging through scattering layers exceeding memory effect range with spatial-correlation-achieved point-spread-function

2018 ◽  
Vol 43 (8) ◽  
pp. 1670 ◽  
Author(s):  
Long Li ◽  
Quan Li ◽  
Shuai Sun ◽  
Hui-Zu Lin ◽  
Wei-Tao Liu ◽  
...  
Keyword(s):  
Spatial Correlation ◽  
Memory Effect ◽  
Point Spread Function ◽  
Point Spread ◽  
Spread Function

scholarly journals Characterizing and Mitigating Sensor Generated Spatial Correlations in Airborne Hyperspectral Imaging Data

2020 ◽  
Vol 12 (4) ◽  
pp. 641 ◽  
Author(s):  
Deep Inamdar ◽  
Margaret Kalacska ◽  
George Leblanc ◽  
J. Pablo Arroyo-Mora
Keyword(s):  
Spatial Variability ◽  
Spatial Correlation ◽  
Hyperspectral Imaging ◽  
Point Spread Function ◽  
Spatial Correlations ◽  
Spectral Variability ◽  
Point Spread Functions ◽  
Point Spread ◽  
Spatial Correlation Structure ◽  
Spread Function

In hyperspectral imaging (HSI), the spatial contribution to each pixel is non-uniform and extends past the traditionally square spatial boundaries designated by the pixel resolution, resulting in sensor-generated blurring effects. The spatial contribution to each pixel can be characterized by the net point spread function, which is overlooked in many airborne HSI applications. The objective of this study was to characterize and mitigate sensor blurring effects in airborne HSI data with simple tools, emphasizing the importance of point spread functions. Two algorithms were developed to (1) quantify spatial correlations and (2) use a theoretically derived point spread function to perform deconvolution. Both algorithms were used to characterize and mitigate sensor blurring effects on a simulated scene with known spectral and spatial variability. The first algorithm showed that sensor blurring modified the spatial correlation structure in the simulated scene, removing 54.0%–75.4% of the known spatial variability. Sensor blurring effects were also shown to remove 31.1%–38.9% of the known spectral variability. The second algorithm mitigated sensor-generated spatial correlations. After deconvolution, the spatial variability of the image was within 23.3% of the known value. Similarly, the deconvolved image was within 6.8% of the known spectral variability. When tested on real-world HSI data, the algorithms sharpened the imagery while characterizing the spatial correlation structure of the dataset, showing the implications of sensor blurring. This study substantiates the importance of point spread functions in the assessment and application of airborne HSI data, providing simple tools that are approachable for all end-users.

Point spread function based feature optimization and fusion for underwater objet recognition

2013 ◽  
Vol 26 (11) ◽  
pp. 944-952 ◽  
Author(s):  
Huibin Wang ◽  
Rong Zhang ◽  
Zhe Chen ◽  
Lizhong Xu ◽  
Jie Shen
Keyword(s):  
Point Spread Function ◽  
Point Spread ◽  
Spread Function ◽  
Feature Optimization

Determination of the Point Spread Function of a Computer-Generated Lens Formed by a Phase Light Modulator

2020 ◽  
Vol 128 (7) ◽  
pp. 1036-1040 ◽  
Author(s):  
N. G. Stsepuro ◽  
G. K. Krasin ◽  
M. S. Kovalev ◽  
V. N. Pestereva
Keyword(s):  
Point Spread Function ◽  
Light Modulator ◽  
Point Spread ◽  
Spread Function

Accurate point spread function (PSF) estimation for coded aperture cameras

2014 ◽  
Author(s):  
Jingyu Yang ◽  
Bin Jiang ◽  
Jinlong Ma ◽  
Yi Sun ◽  
Ming Di
Keyword(s):  
Point Spread Function ◽  
Coded Aperture ◽  
Point Spread ◽  
Spread Function ◽  
Psf Estimation

Aberration retrieval from the intensity point-spread function in the focal region using the extended Nijboer–Zernike approach

2005 ◽  
Vol 52 (12) ◽  
pp. 1695-1728 ◽  
Author(s):  
C. Van der Avoort * ◽  
J. J. M. Braat ◽  
P. Dirksen ◽  
A. J. E. M. Janssen
Keyword(s):  
Point Spread Function ◽  
Focal Region ◽  
Point Spread ◽  
Spread Function
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