Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
