According to dichromatic reflection model, the previous methods of specular reflection separation in image processing often separate specular reflection from a single image using patch-based priors. Due to lack of global information, these methods often can鈥檛 completely separate the specular component of an image and are incline to degrade image textures. In this paper, we derive a global color-lines constraint from dichromatic reflection model to effectively recover specular and diffuse reflection. Our key observation is from that each image pixel lies along a color line in normalized RGB space and the different color lines representing distinct diffuse chromaticities intersect at one point, namely the illumination chromaticity. For pixels along the same color line, they spread over the entire image and their distances to the illumination chromaticity reflects the amount of specular reflection components. With global (non-local) information from these color lines, our method can effectively separate specular and diffuse reflection components in a pixel-wise way for a single image, and it is suitable for real-time applications. Our experimental results on synthetic and real images show that our method performs better than the state-of-the-art methods to separate specular reflection.

This work was published on IEEE Transactions on Image Processing ,2017:1-11. titled Specular Reflection Separation with Color-Lines Constraint.