Detection of highlights is a prominent issue in computer vision, graphics. Many algorithms in image processing assume diffuse only reflections and deem specular reflections to be outliers. However, in the real world, the presence of specular reflections is inevitable since there are many dielectric inhomogeneous objects which have both diffuse and specular reflections. Hence, detection, and sometimes removal, of specular reflection (highlights) in an image may be critical. Proposed work takes help of global color-lines information to effectively recover specular and diffuse reflection. Our critical observation is that each image pixel lies along a color line in normalized RGB space and the different color lines meaning distinct diffuse chromaticities intersect at one point, known to be the illumination chromaticity. Pixels along the same color line, spread over the entire image and their distances to the illumination chromaticity give the number of specular reflection components. With such non-local overall information from these color lines, our method can effectively separate specular and diffuse reflection components in a pixel-wise way. We also present an efficient algorithm for complete removal of the highlights and it is suitable for real-time applications.