Authors:
Suil Son
;
Deokyoung Kang
and
Suk I. Yoo
Affiliation:
Seoul National University, Korea, Republic of
Keyword(s):
Denoising, Non-local Means, Total Variation Regularization, Non-local Total Variation Regularization, Non-local H1 Regularization.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Computer Vision, Visualization and Computer Graphics
;
Convex Optimization
;
Health Engineering and Technology Applications
;
Image Understanding
;
Pattern Recognition
;
Signal Processing
;
Software Engineering
;
Theory and Methods
Abstract:
Non-local Huber regularization is proposed for image denoising. This method improves the non-local total variation regularization and the non-local H1 regularization approaches. The non-local total variation regularization preserves edges better than the non-local H1 regularization; however, it leaves a little noise. In contrast, the non-local H1 regularization eliminates noise better than the non-local total variation regularization; however, it blurs edges. To take both advantages of the two methods, the proposed method applies the non-local
total variation to large non-local intensity differences and applies the non-local H1 regularization to small non-local intensity differences. A boundary value to determine whether the intensity difference comes from edges or noise is also suggested. The experimental results of the proposed method is compared to the result from the non-local total variation regularization and to the result from the non-local H1 regularization; The effect of the
boundary value is illustrated as PSNR changes with respect to the various values of the boundary values.
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