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Mathematical Problems in Engineering
Volume 2015, Article ID 979415, 15 pages
Research Article

Weighted Nuclear Norm Minimization Based Tongue Specular Reflection Removal

School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China

Received 4 May 2015; Accepted 29 July 2015

Academic Editor: Thomas Schuster

Copyright © 2015 Zhenchao Cui et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In computational tongue diagnosis, specular reflection is generally inevitable in tongue image acquisition, which has adverse impact on the feature extraction and tends to degrade the diagnosis performance. In this paper, we proposed a two-stage (i.e., the detection and inpainting pipeline) approach to address this issue: (i) by considering both highlight reflection and subreflection areas, a superpixel-based segmentation method was adopted for the detection of the specular reflection areas; (ii) by extending the weighted nuclear norm minimization (WNNM) model, a nonlocal inpainting method is proposed for specular reflection removal. Experimental results on synthetic and real images show that the proposed method is accurate in detecting the specular reflection areas and is effective in restoring tongue image with more natural texture information of tongue body.