Effect of SNR Estimation in Defocus Blurred Image Restoration

2012 ◽  
Vol 239-240 ◽  
pp. 1113-1117
Author(s):  
Feng Qing Qin
Keyword(s):  
Image Restoration ◽  
Signal To Noise Ratio ◽  
Parameter Analysis ◽  
Snr Estimation ◽  
Blind Image Restoration ◽  
Blurred Image ◽  
Local Deviation ◽  
Restoration Method ◽  
Ringing Effect ◽  
Blind Image

A blind image restoration method is proposed to improve the quality of the image blurred by camera defocus and system noise. Firstly, the focus point spread function (PSF) of the blurred image is estimated through error-parameter analysis method. Secondly, the Signal-to-Noise Ratio (SNR) of the blurred image is estimated through local deviation method. Thirdly, utilizing the estimated defocus PSF and SNR, image restoration is performed through Wiener filtering method, in which circulation boundary method is adopted to reduce ringing effect. Experimental results show that the SNR of the blurred image is estimated approximately, and verify the great effect of SNR estimation in blind image restoration.

An Enhanced NAS-RIF Algorithm for Blind Image Restoration Based on Total Variation Regularization

2013 ◽  
Vol 423-426 ◽  
pp. 2522-2525
Author(s):  
Xin Ke Li ◽  
Chao Gao ◽  
Yong Cai Guo ◽  
Yan Hua Shao
Keyword(s):  
Image Restoration ◽  
Total Variation ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Tikhonov Regularization Method ◽  
Total Variation Regularization ◽  
Inverse Filtering ◽  
Blind Image Restoration ◽  
Spatially Adaptive ◽  
Blind Image

In order to improve the quality of blind image restoration, we propose an algorithm which combines Non-negativity and Support constraint Recursive Inverse Filtering (NAS-RIF) and adaptive total variation regularization. In the proposed algorithm, the total variation regularization constraint term is added in the NAS-RIF algorithm cost function. The majorization-minimization approach and conjugate gradient iterative algorithm are adopted to improve the convergence speed. We do the simulation experiments for the blurred classic test image which is added additive random noise. Experimental results show that the restoration effect of our algorithm is better than the spatially adaptive Tikhonov regularization method and the NAS-RIF spatially adaptive regularization algorithm, while the value of improvement of signal to noise ratio (ISNR) has improved.

scholarly journals Spectral-Based Blind Image Restoration Method for Thin TOMBO Imagers

Sensors ◽  
2008 ◽  
Vol 8 (9) ◽  
pp. 6108-6124 ◽  
Author(s):  
Amar El-Sallam ◽  
Farid Boussaid
Keyword(s):  
Image Restoration ◽  
Blind Image Restoration ◽  
Restoration Method ◽  
Blind Image

scholarly journals Restoration for Motion Blurred Images of Moving Objects

2012 ◽  
Vol 6-7 ◽  
pp. 1108-1111
Author(s):  
Gui Xiang Zhou
Keyword(s):  
Image Restoration ◽  
Moving Objects ◽  
Wiener Filter ◽  
Parameter Analysis ◽  
Blurred Image ◽  
Probability And Statistics ◽  
Blurred Images ◽  
Ringing Effect ◽  
Transportation Applications ◽  
Error Parameter

Image restoration plays an important role in transportation applications. This paper studies a motion blurred image processing method, which has good recovery effect. In this method, first the wiener filter is used for image restoration. Then, based on the error parameter analysis, the parameters of point spread function are estimated, and the noise parameter is estimated by using the probability and statistics method. Furthermore, the ringing effect is processed by using the smooth boundary method. Finally, experimental results show that the proposed method can restore the motion blurred images effectively and has strong robustness for the noise.

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