Image restoration of multiple noisy images by use of a priori knowledge of the anisoplanatic point-spread function

1998 ◽  
Vol 23 (2) ◽  
pp. 83 ◽  
Author(s):  
D. R. Gerwe ◽  
M. A. Plonus
Keyword(s):  
Image Restoration ◽  
Point Spread Function ◽  
A Priori ◽  
A Priori Knowledge ◽  
Noisy Images ◽  
Point Spread ◽  
Spread Function ◽  
Priori Knowledge

Image restoration with an a-priori estimation of the point spread function

1999 ◽  
Author(s):  
Konstantin T. Protasov ◽  
Vladimir V. Belov ◽  
Nikolai V. Molchunov
Keyword(s):  
Image Restoration ◽  
Point Spread Function ◽  
A Priori ◽  
Point Spread ◽  
Spread Function ◽  
A Priori Estimation

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

RANGE ESTIMATION FROM CAMERA BLUR BY REGULARIZED ADAPTIVE IDENTIFICATION

1994 ◽  
Vol 08 (06) ◽  
pp. 1273-1300 ◽  
Author(s):  
LEE F. HOLEVA
Keyword(s):  
Point Spread Function ◽  
Adaptive Algorithm ◽  
A Priori ◽  
Conveyor Belt ◽  
Range Image ◽  
Range Estimation ◽  
Point Spread ◽  
Spread Function ◽  
Solution Point ◽  
Object Depth

One of the fundamental problems of machine vision is the estimation of object depth from perceived images. This paper describes both an apparatus and the corresponding algorithms for the passive extraction of object depth. Here passive extraction implies the processing of images acquired using only the existing illumination, in this case roughly uniform white light. Depth from defocused algorithms are extremely sensitive to image variations. Regularization, the application of a priori constraints, is employed to improve the accuracy of the range measurements. When the camera’s point spread function is shift invariant, an adaptive algorithm is developed in the frequency domain. The constraints imposed upon the solution power spectrum vector vary temporally. When the camera’s point spread function is shift varying, an adaptive algorithm is developed in the spatial domain. The constraints imposed upon the solution point spread vector vary spatially. Data is acquired from line scan cameras. Only a single range measurement or a single depth profile is extracted. By relying upon the motion of the observed object on a conveyor belt, a complete range image may be generated.

scholarly journals Radiometric and geometric characteristics of Pleiades images

2014 ◽  
Vol XL-1 ◽  
pp. 173-177 ◽  
Author(s):  
K. Jacobsen ◽  
H. Topan ◽  
A. Cam ◽  
M. Özendi ◽  
M. Oruc
Keyword(s):  
Image Restoration ◽  
Point Spread Function ◽  
Image Noise ◽  
Image Resolution ◽  
The Other ◽  
Edge Enhancement ◽  
Space Images ◽  
Point Spread ◽  
Spread Function ◽  
Nominal Resolution

Pleiades images are distributed with 50 cm ground sampling distance (GSD) even if the physical resolution for nadir images is just 70 cm. By theory this should influence the effective GSD determined by means of point spread function at image edges. Nevertheless by edge enhancement the effective GSD can be improved, but this should cause enlarged image noise. Again image noise can be reduced by image restoration. Finally even optimized image restoration cannot improve the image information from 70 cm to 50 cm without loss of details, requiring a comparison of Pleiades image details with other very high resolution space images. The image noise has been determined by analysis of the whole images for any sub-area with 5 pixels times 5 pixels. Based on the standard deviation of grey values in the small sub-areas the image noise has been determined by frequency analysis. This leads to realistic results, checked by test targets. On the other hand the visual determination of image noise based on apparently homogenous sub-areas results in too high values because the human eye is not able to identify small grey value differences – it is limited to just approximately 40 grey value steps over the available gray value range, so small difference in grey values cannot be seen, enlarging results of a manual noise determination. <br><br> A tri-stereo combination of Pleiades 1A in a mountainous, but partially urban, area has been analyzed and compared with images of the same area from WorldView-1, QuickBird and IKONOS. The image restoration of the Pleiades images is very good, so the effective image resolution resulted in a factor 1.0, meaning that the effective resolution corresponds to the nominal resolution of 50 cm. This does not correspond to the physical resolution of 70 cm, but by edge enhancement the steepness of the grey value profile across the edge can be enlarged, reducing the width of the point spread function. Without additional filtering edge enhancement enlarges the image noise, but the average image noise of approximately 1.0 grey values related to 8 bit images is very small, not indicating the edge enhancement and the down sampling of the GSD from 70 cm to 50 cm. So the direct comparison with the other images has to give the answer if the image quality of Pleiades images is on similar level as corresponding to the nominal resolution. As expected with the image geometry there is no problem. This is the case for all used space images in the test area, where the point identification limits the accuracy of the scene orientation.

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