Deep Colorization
Zezhou Cheng, Qingxiong Yang, Bin Sheng

TL;DR
This paper presents a fully automatic deep learning-based colorization method that leverages large-scale reference databases and advanced post-processing to produce high-quality, artifact-free colorized images efficiently.
Contribution
It introduces a novel deep learning framework for automatic colorization that integrates large-scale reference data, adaptive clustering, and bilateral filtering for superior results.
Findings
Outperforms state-of-the-art algorithms in quality and speed
Effectively handles large reference databases despite patch matching noise
Produces artifact-free, high-quality colorized images
Abstract
This paper investigates into the colorization problem which converts a grayscale image to a colorful version. This is a very difficult problem and normally requires manual adjustment to achieve artifact-free quality. For instance, it normally requires human-labelled color scribbles on the grayscale target image or a careful selection of colorful reference images (e.g., capturing the same scene in the grayscale target image). Unlike the previous methods, this paper aims at a high-quality fully-automatic colorization method. With the assumption of a perfect patch matching technique, the use of an extremely large-scale reference database (that contains sufficient color images) is the most reliable solution to the colorization problem. However, patch matching noise will increase with respect to the size of the reference database in practice. Inspired by the recent success in deep learning…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Visual Attention and Saliency Detection · Generative Adversarial Networks and Image Synthesis
MethodsColorization
