Automatic grayscale image colorization using histogram regression

Colorization aims to adding colors to a grayscale image. This task is ill-posed in the sense that assigning the colors to a grayscale image without any prior knowledge is ambiguous. Most of the previous methods require some amount of user interventions, making colorization a hard work. Motivated by this, a novel automatic grayscale image colorization method based on histogram regression is presented in this paper. A source image is adopted to provide the color information. Locally weighted regression is performed on both the grayscale image and the source image. Thus, the feature distributions of two images can be obtained. Then, a new matching method is proposed to align these features by finding and adjusting the zero-points of the histogram. When the luminance-color correspondence was achieved, the grayscale image is colorized in a weighted way. Moreover, a new evaluation method is specially designed to assess the confidence of the colorization results. Various experiment results are given to show the validity of this method.

► We proposed a new automatic image colorization method based on histogram regression.
► The image feature was matched features by finding and adjusting the zero-points.
► The luminance-color correspondence was achieved in a weighted way for colorization.
► A novel evaluation method is special designed to assess the colorization results.