Optimal regularization parameter estimation for image restoration

1991 ◽  
Author(s):  
Stanley J. Reeves ◽  
Russell M. Mersereau
Keyword(s):  
Parameter Estimation ◽  
Image Restoration ◽  
Regularization Parameter

scholarly journals SIMRES-TV: NOISE AND RESIDUAL SIMILARITY FOR PARAMETER ESTIMATION IN TOTAL VARIATION

2021 ◽  
Vol XLIV-2/W1-2021 ◽  
pp. 171-176
Author(s):  
V. B. S. Prasath ◽  
N. N. Hien ◽  
D. N. H. Thanh ◽  
S. Dvoenko
Keyword(s):  
Image Processing ◽  
Parameter Estimation ◽  
Image Restoration ◽  
Total Variation ◽  
Regularization Parameter ◽  
Noise Levels ◽  
Split Bregman ◽  
Edge Preserving ◽  
Restoration Scheme ◽  
Split Bregman Algorithm

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.

Astronomical image restoration based on maximum-likelihood incorporated parameter estimation

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
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Image Restoration ◽  
Astronomical Image
Sign in / Sign up

Export Citation Format

Share Document