Transforming Color: A Novel Image Colorization Method

TL;DR

This paper presents a new image colorization technique combining transformers and GANs to produce more realistic and globally consistent colorized images, outperforming existing methods.

Contribution

It introduces a novel colorization approach that leverages transformer architecture for global context and GANs for enhanced visual quality, which is a significant advancement over prior methods.

Findings

Outperforms state-of-the-art colorization techniques

Captures long-range dependencies effectively

Produces more realistic and visually appealing images

Abstract

This paper introduces a novel method for image colorization that utilizes a color transformer and generative adversarial networks (GANs) to address the challenge of generating visually appealing colorized images. Conventional approaches often struggle with capturing long-range dependencies and producing realistic colorizations. The proposed method integrates a transformer architecture to capture global information and a GAN framework to improve visual quality. In this study, a color encoder that utilizes a random normal distribution to generate color features is applied. These features are then integrated with grayscale image features to enhance the overall representation of the images. Our method demonstrates superior performance compared with existing approaches by utilizing the capacity of the transformer, which can capture long-range dependencies and generate a realistic colorization of the GAN. Experimental results show that the proposed network significantly outperforms other state-of-the-art colorization techniques, highlighting its potential for image colorization. This research opens new possibilities for precise and visually compelling image colorization in domains such as digital restoration and historical image analysis.