Weighted Nuclear Norm Minimization Based Tongue Specular Reflection Removal

Abstract

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.

Removal of specular reflections from image sequences using feature correspondences

“…The detection and removal of specular reflections from images has been an area of interest to the Computer Vision community for many years, and existing techniques for this task find applications in medical science [10][11][12], video surveillance [13], image refinement and image reconstruction [15][16][17]. Citing the importance of the availability of such methods, there have been various attempts to address this issue.…”

A Truncated Matrix Completion Algorithm Using Prior Information for Wind Turbine Clutter Suppression

“…Due to the low-rank structure of the sparse matrix, only a few large singular values affect the result of MC. However, when the singular values of the nuclear norm are thresholded with the same constant, the information of large singular values will be lost [20], and then, the recovered data will generate a low peak signal-tonoise ratio (SNR), which greatly limits its ability and flexibility to deal with many practical problems such as denoising.…”