TIC: Text-Guided Image Colorization
Subhankar Ghosh, Prasun Roy, Saumik Bhattacharya, Umapada Pal, Michael, Blumenstein

TL;DR
This paper introduces TIC, a novel deep learning model that incorporates textual descriptions as auxiliary input to improve the accuracy and realism of image colorization, addressing the challenge of ill-posed colorization tasks.
Contribution
It is one of the first models to integrate textual conditioning into the image colorization process, enhancing color fidelity and realism.
Findings
Outperforms existing colorization methods both qualitatively and quantitatively.
Utilizes textual descriptions to guide the colorization process effectively.
Demonstrates improved color accuracy and scene consistency.
Abstract
Image colorization is a well-known problem in computer vision. However, due to the ill-posed nature of the task, image colorization is inherently challenging. Though several attempts have been made by researchers to make the colorization pipeline automatic, these processes often produce unrealistic results due to a lack of conditioning. In this work, we attempt to integrate textual descriptions as an auxiliary condition, along with the grayscale image that is to be colorized, to improve the fidelity of the colorization process. To the best of our knowledge, this is one of the first attempts to incorporate textual conditioning in the colorization pipeline. To do so, we have proposed a novel deep network that takes two inputs (the grayscale image and the respective encoded text description) and tries to predict the relevant color gamut. As the respective textual descriptions contain color…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Advanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques
MethodsColorization
