Laplacian-guided image decolorization

Abstract

In this paper we introduce a novel decolorization strategy built on image fusion principles. Decolorization (color-to-grayscale), is an important transformation used in many monochrome image processing applications. We demonstrate that aside from color spatial distribution, local information plays an important role in maintaining the discriminability of the image conversion. Our strategy blends the three color channels R, G, B guided by two weight maps that filter the local transitions and measure the dominant values of the regions using the Laplacian information. In order to minimize artifacts introduced by the weight maps, our fusion approach is designed in a multi-scale fashion, using a Laplacian pyramid decomposition. Additionally, compared with most of the existing techniques our straightforward technique has the advantage to be computationally effective. We demonstrate that our technique is temporal coherent being suitable to decolorize videos. A comprehensive qualitative and also quantitative evaluation based on an objective visual descriptor demonstrates the utility of our decolorization technique.