Selection of regularization parameter based on synchronous noise in total variation image restoration

2011 ◽  
Author(s):  
Peng Liu ◽  
Dingsheng Liu ◽  
Zhiwen Liu
Keyword(s):  
Image Restoration ◽  
Total Variation ◽  
Regularization Parameter ◽  
Selection Of

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.

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