A Research on the Optimization of Fuzzy Image

2013 ◽  
Vol 409-410 ◽  
pp. 1593-1596
Author(s):  
Xue Feng Wu ◽  
Yu Fan
Keyword(s):  
Mathematical Model ◽  
Image Restoration ◽  
Point Spread Function ◽  
Motion Blur ◽  
Inverse Filtering ◽  
Point Spread ◽  
Spread Function ◽  
Blurred Image ◽  
Blurred Images ◽  
Fuzzy Image

The restore algorithm of the image blurred by motion is proposed, and a mathematical model based on motion blur system is eomtrueted£®The Point spread function of the motion blur is given According to the characteristics of blurred images the parameters of point spread function are estimated ,and three methods are introduced for image restoration. The three methods are inverse filtering of image restoration, Lucy-Richardson image restoration and Wiener image restoration. The principles of the three image restoration methods are analyzed. The motion blurred image restoration experiment is made. The results show that the visibility of the image is improved, and the image restoration is more stable.

Study on Motion Blur Image Restoration Algorithms

2013 ◽  
Vol 753-755 ◽  
pp. 2976-2979
Author(s):  
Yu Fan ◽  
Xue Feng Wu
Keyword(s):  
Mathematical Model ◽  
Image Restoration ◽  
Point Spread Function ◽  
Motion Blur ◽  
Inverse Filtering ◽  
Point Spread ◽  
Spread Function ◽  
Blurred Image ◽  
Restoration Algorithms ◽  
Blurred Images

The restore algorithm of the image blurred by motion is proposed, and a mathematical model based on motion blur system is eomtrueted£®The Point spread function of the motion blur is given£®According to the characteristics of blurred images£¬the parameters of point spread function are estimated ,and three methods are introduced for image restoration. The three methods are inverse filtering of image restoration,Lucy-Richardson image restoration and Wiener image restoration.The principles of the three image restoration methods are analyzed. The motion blurred image restoration experiment is made. The results show that the visibility of the image is improved ,and the image restoration is more stable.

The Restoration of Motion Blurred Images Based on the Background Modeling

2014 ◽  
Vol 687-691 ◽  
pp. 3591-3595
Author(s):  
Jiang Yang Chen ◽  
Xi Ling Luo
Keyword(s):  
Wiener Filter ◽  
Background Modeling ◽  
Motion Blur ◽  
Edge Density ◽  
Image Generation ◽  
Point Spread ◽  
Spread Function ◽  
Blurred Image ◽  
Image Area ◽  
Blurred Images

For the mutual effects of camera shake and subject movement, the image generation space varying motion blur. In order to achieve image restoration, firstly dividing the image area using the Gaussian background modeling, and updated model adaptive to improve the speed and convergence accuracy. Then use the total variation (TV) of the L1 model to estimate the regional point spread function (PSF), and adopted the edge density weight to reduce small edge’s interference for the PSF estimates. Eventually to restored image by Wiener filter. Through experimental analysis, compared with other algorithms, our algorithms get better results in the space varies motion-blurred image.

Wiener Filter Based on the Image to Blur

2012 ◽  
Vol 591-593 ◽  
pp. 1567-1570
Author(s):  
Chao Da Chen ◽  
Si Qing Zhang ◽  
Chui Xin Chen
Keyword(s):  
Image Quality ◽  
Image Restoration ◽  
Point Spread Function ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Point Spread ◽  
Spread Function ◽  
Degraded Image ◽  
Ill Posed ◽  
Fuzzy Image

Image restoration, refers to the removal or loss in the process of getting digital image degradation of the image quality, image restoration technology is the key to meet the requirements of the point spread function, degradation model is an ill-posed integral equations, in the frequency domain, when H ( U, V ) less or equal to zero, the noise will be amplified, the degraded image and interference in H ( U, V ) value of the spectrum will be small to restore the image influence. In view of the point spread function put forward Wiener filtering algorithm, the noise lead to ill-posed integral with specified signal-to-noise ratio to reduce image restoration effects, through the IPT toolbox for fuzzy image restoration, image quality to achieve the anticipated effect.

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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