# Image Colorization: A Survey and Dataset

arXiv: https://arxiv.org/abs/2008.10774 · HTML: https://mlsanity.com/paper/2008.10774/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 23 of 23)

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- [MASS: Overcoming Language Bias in Image-Text Matching](https://mlsanity.com/paper/2501.11469/) (2025)
- [ViT-Inception-GAN for Image Colourising](https://mlsanity.com/paper/2106.06321/) (2021)
- [Cascading Modular Network (CAM-Net) for Multimodal Image Synthesis](https://mlsanity.com/paper/2106.09015/) (2021)
- [Human vs Objective Evaluation of Colourisation Performance](https://mlsanity.com/paper/2204.05200/) (2022)
- [MMC: Multi-Modal Colorization of Images using Textual Descriptions](https://mlsanity.com/paper/2304.11993/) (2023)
- [Scene Graph Conditioning in Latent Diffusion](https://mlsanity.com/paper/2310.10338/) (2023)
- [Computer-aided Colorization State-of-the-science: A Survey](https://mlsanity.com/paper/2410.02288/) (2024)
- [Automatic Image Colorization with Convolutional Neural Networks and Generative Adversarial Networks](https://mlsanity.com/paper/2508.05068/) (2025)
- [MFmamba: A Multi-function Network for Panchromatic Image Resolution Restoration Based on State-Space Model](https://mlsanity.com/paper/2511.18888/) (2025)
- [MCIQA-2K: A Multi-Dimensional Dataset and No-Reference Quality Assessment Benchmark for Colorized Images](https://mlsanity.com/paper/2609.14495/) (2026)

## Abstract (arXiv)

Image colorization estimates RGB colors for grayscale images or video frames to improve their aesthetic and perceptual quality. Over the last decade, deep learning techniques for image colorization have significantly progressed, necessitating a systematic survey and benchmarking of these techniques. This article presents a comprehensive survey of recent state-of-the-art deep learning-based image colorization techniques, describing their fundamental block architectures, inputs, optimizers, loss functions, training protocols, training data, etc. It categorizes the existing colorization techniques into seven classes and discusses important factors governing their performance, such as benchmark datasets and evaluation metrics. We highlight the limitations of existing datasets and introduce a new dataset specific to colorization. We perform an extensive experimental evaluation of existing image colorization methods using both existing datasets and our proposed one. Finally, we discuss the limitations of existing methods and recommend possible solutions and future research directions for this rapidly evolving topic of deep image colorization. The dataset and codes for evaluation are publicly available at https://github.com/saeed-anwar/ColorSurvey.