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A Proposed Grayscale Face Image Colorization System using Particle Swarm Optimization

A Proposed Grayscale Face Image Colorization System using Particle Swarm Optimization
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Author(s): Abul Hasnat (Government College of Engineering and Textile Technology, Berhampore, West Bengal, India), Santanu Halder (Government College of Engineering and Leather Technology, Kolkata, India), Debotosh Bhattacharjee (Department of Computer Science and Engineering, Jadavpur University, Kolkata, India) and Mita Nasipuri (Department of Computer Science and Engineering, Jadavpur University, Kolkata, India)
Copyright: 2017
Volume: 1
Issue: 1
Pages: 18
Source title: International Journal of Virtual and Augmented Reality (IJVAR)
Editor(s)-in-Chief: Cristina Portales Ricart (Universitat de Valencia, Spain) and Marcos Fernández Marin (Universidad de Valencia, Spain)
DOI: 10.4018/IJVAR.2017010106

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Abstract

The proposed work is a novel grayscale face image colorization approach using a reference color face image. It takes a reference color image which presumably contains semantically similar color information for the query grayscale image and colorizes the grayscale face image with the help of the reference image. In this novel patch based colorization, the system searches a suitable patch on reference color image for each patch of grayscale image to colorize. Exhaustive patch search in reference color image takes much time resulting slow colorization process applicable for real time applications. So PSO is used to reduce the patch searching time for faster colorization process applicable in real time applications. The proposed method is successfully applied on 150 male and female face images of FRAV2D database. “Colorization Turing test” was conducted asking human subject to choose the image(close to the original color image) between colorized image using proposed algorithm and recent methods and in most of the cases colorized images using the proposed method got selected.

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