scholarly journals Application of Blind Deconvolution Algorithm for Deblurring of Saturated Images

2019 ◽  
Vol 8 (2S3) ◽  
pp. 1383-1386
Keyword(s):  
Image Processing ◽  
Image Restoration ◽  
Point Spread Function ◽  
Blind Deconvolution ◽  
Visual Quality ◽  
Visual Examination ◽  
Point Spread ◽  
Spread Function ◽  
Sharp Image ◽  
Deblurring And Denoising

Image Restoration is a field of Image Processing which manages recuperating a unique and sharp image from a debased image utilizing a numerical corruption and reclamation model. This investigation centers around rebuilding of corrupted images which have been obscured by known or obscure debasement work. Image rebuilding which reestablishes an unmistakable image from a solitary haze image is a troublesome issue of assessing two questions: a point spread function (PSF) and its optimal image. Image deblurring can improve visual quality and mitigates movement obscure for dynamic visual examination. We propose a strategy to deblur immersed images for dynamic visual examination by applying obscure piece estimation and deconvolution demonstrating. The haze portion is assessed in a change space, though the deconvolution model is decoupled into deblurring and denoising stages by means of variable part

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

On continuous image models and image analysis in the presence of correlated noise

1990 ◽  
Vol 22 (2) ◽  
pp. 332-349 ◽  
Author(s):  
Peter Hall ◽  
Inge Koch
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Point Spread Function ◽  
Convergence Rates ◽  
Discrete Distributions ◽  
Correlated Noise ◽  
Point Spread ◽  
Special Cases ◽  
Spread Function ◽  
Image Models

Most theoretical studies of image processing employ discrete image models. While that might be a good approximation to digital analysis, it severely restricts the class of tractable models for the blur component of image degradation, and concentrates excessive attention on specialized features of the pixel lattice. It is analogous to modelling all real statistical data using discrete distributions, which is clearly unnecessary. In this paper we study a continuous model for image analysis, in the presence of systematic degradation via a point spread function and stochastic degradation by a second-order stationary random field. Thus, we depart from the restrictive white-noise models which are commonly used in the theory of image analysis. We establish a general result which describes the performance of optimal image processing methods when the noise process has short-range dependence. Concise limits to resolution are derived, depending on image type, point spread function and noise correlation. These results are developed in important special cases, giving explicit formulae for optimal smoothing sets and convergence rates.

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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