This study tackles the image color to gray conversion problem. The aim was to understand the conversion qualities that can improve the accuracy of results when the grayscale conversion is applied as a pre-processing step in the context of vision algorithms, and in particular dense stereo matching. We evaluated many different state of the art color to grayscale conversion algorithms. We also propose an ad-hoc adaptation of the most theoretically promising algorithm, which we call Multi-Image Decolorize (MID). This algorithm comes from an in-depth analysis of the existing conversion solutions and consists of a multi-image extension of the algorithm by Grundland and Dodgson (The decolorize algorithm for contrast enhancing, color to grayscale conversion, Tech. Rep. UCAM-CL-TR-649, University of Cambridge, 2005) which is based on predominant component analysis. In addition, two variants of this algorithm have been proposed and analyzed: one with standard unsharp masking and another with a chromatic weighted unsharp masking technique (Nowak and Baraniuk in IEEE Trans Image Process 7(7): 1068-1074, 1998) which enhances the local contrast as shown in the approach by Smith et al. (Comput Graph Forum 27(2), 2008). We tested the relative performances of this conversion with respect to many other solutions, using the StereoMatcher test suite (Scharstein and Szeliski in Int J Comput Vis 47(1-3): 7-42, 2002) with a variety of different datasets and different dense stereo matching algorithms. The results show that the overall performance of the proposed MID conversion are good and the reported tests provided useful information and insights on how to design color to gray conversion to improve matching performance. We also show some interesting secondary results such as the role of standard unsharp masking vs. chromatic unsharp masking in improving correspondence matching.
