A learning-based approach for automatic image and video colorization

TL;DR

This paper introduces a machine learning-based algorithm that automatically colorizes grayscale images and videos using superpixels, achieving high spatial consistency and efficiency without user intervention.

Contribution

It presents a novel superpixel-based learning approach for automatic image and video colorization that improves spatial consistency and processing speed.

Findings

Outperforms existing colorization methods in effectiveness.

Provides high spatial consistency in colorized images.

Operates efficiently without user input.

Abstract

In this paper, we present a color transfer algorithm to colorize a broad range of gray images without any user intervention. The algorithm uses a machine learning-based approach to automatically colorize grayscale images. The algorithm uses the superpixel representation of the reference color images to learn the relationship between different image features and their corresponding color values. We use this learned information to predict the color value of each grayscale image superpixel. As compared to processing individual image pixels, our use of superpixels helps us to achieve a much higher degree of spatial consistency as well as speeds up the colorization process. The predicted color values of the gray-scale image superpixels are used to provide a 'micro-scribble' at the centroid of the superpixels. These color scribbles are refined by using a voting based approach. To generate the final colorization result, we use an optimization-based approach to smoothly spread the color scribble across all pixels within a superpixel. Experimental results on a broad range of images and the comparison with existing state-of-the-art colorization methods demonstrate the greater effectiveness of the proposed algorithm.