A novel and effective method for specular detection and removal by tensor voting

Abstract

Most specular detection methods assumed that dominant highlight regions should be uniform for the detection of highlights, which may not be the case in real images. Even when non-uniformity is allowed in the detection, the specular removal can still suffer from non-converged artifacts due to discontinuities in surface colors, especially in highly textured and multicolor images. In this paper, we propose a novel and effective resolution to separate and remove specular components from a single image by adopting tensor voting to obtain reflectance distribution of an input image. Specular and noise pixels denoted as small tensors are isolated and removed. Diffuse reflectance distribution is achieved by analyzing salient and orientation information of tensors around the specular region. The proposed method is non-iterative and does not require any pre-defined constraints in the input image. We evaluate our proposed method on a dataset consisting of highly textured and multicolor images. Experimental results showed that our result is outstanding compared to other state-of-the-art techniques.

Dim the Lights! -- Low-Rank Prior Temporal Data for Specular-Free Video Recovery

“…Nevertheless, the necessity of having multiple images with specific varying conditions, or of having specific hardware assistance, limits their applicability to general cases. The later overcomes this by utilizing a single image and relying on neighborhood [17,18,19,20,21] or color space [22,23,24,2,25] analysis and propagation. Those approaches however cannot handle large highlight areas and might lead to false specularity detection.…”