Image restoration in computed tomography: estimation of the spatially variant point spread function

1992 ◽  
Vol 11 (4) ◽  
pp. 539-545 ◽  
Author(s):  
SD. Rathee ◽  
Z.J. Koles ◽  
T.R. Overton
Keyword(s):  
Computed Tomography ◽  
Image Restoration ◽  
Point Spread Function ◽  
Point Spread ◽  
Spread Function ◽  
Spatially Variant

Parallel image restoration with spatially variant point spread function: description and first clinical results

2001 ◽  
Author(s):  
Andrew Shearer ◽  
Gerard Gorman ◽  
Triona O'Doherty ◽  
Wilhelm J. van der Putten ◽  
Peter McCarthy ◽  
...  
Keyword(s):  
Image Restoration ◽  
Point Spread Function ◽  
Clinical Results ◽  
Point Spread ◽  
Parallel Image ◽  
Spread Function ◽  
Spatially Variant

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.

Satellite image restoration in the context of a spatially varying point spread function

2010 ◽  
Vol 27 (6) ◽  
pp. 1473 ◽  
Author(s):  
Nasreddine Hajlaoui ◽  
Caroline Chaux ◽  
Guillaume Perrin ◽  
Frédéric Falzon ◽  
Amel Benazza-Benyahia
Keyword(s):  
Image Restoration ◽  
Point Spread Function ◽  
Satellite Image ◽  
Point Spread ◽  
Spread Function ◽  
Spatially Varying
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