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Beyond Landscapes: An Exemplar-based Image colorization method

Saulo Pereira

Overview

This repository contains the source code for my proposed colorization method. It consists of an exemplar-based method which is based on a classification framework and works at superpixel level for enhanced coherence and performance.

It was built upon the project I forked from, but at this stage it holds no resemblance.

The method's pipeline includes third party code:

• The superpixel segmentation in:

Levinshtein, A., Stere, A., Kutulakos, K. N., Fleet, D. J., Dickinson, S. J., & Siddiqi, K. (2009). Turbopixels: Fast superpixels using geometric flows

• The saliency maps in:

Yang, C., Zhang, L., Lu, H., Ruan, X., & Yang, M. H. (2013). Saliency detection via graph-based manifold ranking.

• Histogram matching functions

http://cvhci.anthropomatik.kit.edu/~bschauer/

• The scribble propagation algorithm:

Levin, A., Lischinski, D., & Weiss, Y. (2004, August). Colorization using optimization.

• Parts from the colorization algorithm of:

Gupta, R. K., Chia, A. Y. S., Rajan, D., Ng, E. S., & Zhiyong, H. (2012, October). Image colorization using similar images

Usage

• The script single_colorization performs the colorization for the input pair and parameters defined in the file <./input/single.in>
• Features weights should be adjusted in order to achieve better results for each input pair.

The code was developed for experimental purposes, do not expect more than this.