To solve the problem that the specular highlight covers the surface features during the imaging process and thus hinders many visual applications, this paper proposes a novel specular highlight suppression algorithm based on chromaticity analysis and L1-weighted norm regularization constraints.This method doesn't rely on such preprocessing procedures as clustering and segmentation. By using the distribution of image intensity, the Median Specular-Free（MSF）mechanism is put forward to effectively separate highlight components and suppress noise interference. Meanwhile, by combining the chromaticity analysis with local chromaticity correlation, the L1-weighted norm regularization is carried out. Then, the variables splitting strategy is introduced to realize fast optimization, and the inherent details of the targets were effectively restored, such as their texture, color and edges.Experimental results demonstrate that our method can realize accurate suppression and separation of highlight components, and preserve the original characteristics of the targets to a great extent.This algorithm is highly reliable with increased computational efficiency.