Determination of point spread function for a flat-panel X-ray imager and its application in image restoration

2006 ◽  
Vol 563 (1) ◽  
pp. 167-171 ◽  
Author(s):  
Sungchae Jeon ◽  
Gyuseong Cho ◽  
Young Huh ◽  
Seungoh Jin ◽  
Jongduk Park
Keyword(s):  
Image Restoration ◽  
Point Spread Function ◽  
Flat Panel ◽  
X Ray ◽  
Point Spread ◽  
Spread Function

scholarly journals An On-Orbit Determination of the On-Axis Point Spread Function of the Hinode X-Ray Telescope

2007 ◽  
Vol 59 (sp3) ◽  
pp. S853-S855 ◽  
Author(s):  
Mark Weber ◽  
Edward E. DeLuca ◽  
Leon Golub ◽  
Jonathan Cirtain ◽  
Ryouhei Kano ◽  
...  
Keyword(s):  
Point Spread Function ◽  
Orbit Determination ◽  
X Ray ◽  
Point Spread ◽  
Spread Function

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

Exploring the Parameter Space of Point Spread Function Determination for the Scanning Electron Microscope—Part II: Effect on Image Restoration Quality

2019 ◽  
Vol 25 (05) ◽  
pp. 1183-1194
Author(s):  
Mandy C. Nevins ◽  
Richard K. Hailstone ◽  
Eric Lifshin
Keyword(s):  
Particle Size ◽  
Electron Microscope ◽  
Image Quality ◽  
Scanning Electron Microscope ◽  
Image Restoration ◽  
Point Spread Function ◽  
Background Correction ◽  
Point Spread ◽  
Spread Function ◽  
Scanning Electron

AbstractPoint spread function (PSF) deconvolution is an attractive software-based technique for resolution improvement in the scanning electron microscope (SEM) because it can restore information which has been blurred by challenging operating conditions. In Part 1, we studied a modern PSF determination method for SEM and explored how various parameters affected the method's ability to accurately estimate the PSF. In Part 2, we extend this exploration to PSF deconvolution for image restoration. The parameters include reference particle size, PSF smoothing (K), background correction, and restoration denoising (λ). Image quality was assessed by visual inspection and Fourier analysis. Overall, PSF deconvolution improved image quality. Low λ enhanced image sharpness at the cost of noise, while high λ created smoother restorations with less detail. λ should be chosen to balance feature preservation and denoising based on the application. Reference particle size within ±0.9 nm and K within a reasonable range had little effect on restoration quality. Restorations using background-corrected PSFs had superior quality compared with using no background correction, but if the correction was too high, the PSF was cut off causing blurrier restorations. Future efforts to automatically determine parameters would remove user guesswork, improve this method's consistency, and maximize interpretability of outputs.

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