IMPROVEMENT OF COLORIZATION REALISM VIA THE STRUCTURE TENSOR

Abstract

Colorization is a color manipulation mechanism employing user-assisted color hints for changing grayscale images into colored ones. Several colorization algorithms have been constructed, and many of these methods are able to produce appropriately colorized images given a surprisingly sparse set of hints supplied by the user. However, these color images may not in fact look realistic. Moreover, the contrast in the colorized image may not match the gradient perceived in the original grayscale image. We argue that it is this departure from the original gradient that contributes to the unreal appearance in some colorizations. To correct this, we make use of the Di Zenzo gradient of a color image derived from the structure tensor, and adjust the colorized image such that the Di Zenzo definition of the maximum-contrast gradient agrees with the gradient in the original gray image. We present a heuristic method to this end and guided by this approach devise an optimization-based method. Our gradient projection tends to result in more natural-looking images in the resulting adjusted colorization. To explore the proposed method we utilize minimalist sets of color hints and find in particular that "hotspots" of unrealistic color are subdued into regions of more realistic color. This paper is not aimed at introducing a new basic colorization but instead our method is meant to make any colorization look more realistic; we demonstrate that this is the case for several different basic methods. In fact, we even find that a very simplistic colorization algorithm can be used provided the projection proposed here is then used to make the colorization more realistic looking.