Transforming Color: A Novel Image Colorization Method
Hamza Shafiq, Bumshik Lee

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…
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Taxonomy
MethodsColorization
