Optimal regularization parameter estimation for image restoration

Author(s):  
Stanley J. Reeves ◽  
Russell M. Mersereau
Author(s):  
V. B. S. Prasath ◽  
N. N. Hien ◽  
D. N. H. Thanh ◽  
S. Dvoenko

Abstract. Image restoration with regularization models is very popular in the image processing literature. Total variation (TV) is one of the important edge preserving regularization models used, however, to obtain optimal restoration results the regularization parameter needs to be set appropriately. We propose here a new parameter estimation approach for total variation based image restoration. By utilizing known noise levels we compute the regularization parameter by reducing the similarity between residual and noise variances. We use the split Bregman algorithm for the total variation along with this automatic parameter estimation step to obtain a very fast restoration scheme. Experimental results indicate the proposed parameter estimation obtained better denoised images and videos in terms of PSNR and SSIM measures and the computational overload is less compared with other approaches.


2013 ◽  
Vol 21 (11) ◽  
pp. 2943-2950 ◽  
Author(s):  
耿则勋 GENG Ze-xun ◽  
魏小峰 WEI Xiao-feng ◽  
沈忱 SHEN Chen

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