MINI REVIEW article
Front. Comput. Sci.
Sec. Computer Vision
Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1626641
From Shades to Vibrance: A Comprehensive Review of Modern Image Colorization Techniques
Provisionally accepted
- 1Robert Gordon University, Aberdeen, United Kingdom
- 2Kyoto University of Advanced Science, Kyoto, Japan
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Image colorization has become a significant task in computer vision, addressing the challenge of transforming grayscale images into realistic, vibrant color outputs. Recent advancements leverage deep learning techniques, ranging from generative adversarial networks (GANs) to diffusion models, and integrate semantic understanding, multi-scale features, and user-guided controls. This review explores state-of-the-art methodologies, highlighting innovative components such as semantic class distribution learning, bidirectional temporal fusion, and instance-aware frameworks. Evaluation metrics, including PSNR, FID, and task-specific measures, ensure a comprehensive assessment of performance. Despite remarkable progress, challenges like multimodal uncertainty, computational cost, and generalization remain. This paper provides a thorough analysis of existing approaches, offering insights into their contributions, limitations, and future directions in automated image colorization.
Keywords: Image colorization, Real-Time Colorization, black-and-white colorization, User-guided colorization, ::::::: learning ::::::::::: objectives, :: and :::: user ::::::::::::::: controllability. ::::: This ::::::: section :::::::::::: categorizes ::: and :::::::: reviews ::::::::::::::: state-of-the-art ::::::::::: techniques :::: into :::: key ::::::::::::::: methodological ::::::::: families, ::: each :::::::: offering :::::::: distinct ::::::::::: advantages ::: and ::::::::::: trade-offs. :::: We :::::::: organize :::::: these ::::::::::: approaches ::::: into ::::::::::: discretized ::::::::::::: classification :::::::: models, ::::::::: adversarial :::::::::: networks
Received: 11 May 2025; Accepted: 26 Aug 2025.
Copyright: © 2025 Geenath and Yapa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Oshen Geenath, Robert Gordon University, Aberdeen, United Kingdom
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