# Image Colorization with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1803.05400 · HTML: https://mlsanity.com/paper/1803.05400/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2018.

## Built on by (most cited first, 24 of 24)

- [Domain Impression: A Source Data Free Domain Adaptation Method](https://mlsanity.com/paper/2102.09003/) (2021)
- [ZONE: Zero-Shot Instruction-Guided Local Editing](https://mlsanity.com/paper/2312.16794/) (2023)
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- [MaLP: Manipulation Localization Using a Proactive Scheme](https://mlsanity.com/paper/2303.16976/) (2023)
- [Diffusion-based Document Layout Generation](https://mlsanity.com/paper/2303.10787/) (2023)
- [Focusing on Persons: Colorizing Old Images Learning from Modern Historical Movies](https://mlsanity.com/paper/2108.06515/) (2021)
- [AE-StyleGAN: Improved Training of Style-Based Auto-Encoders](https://mlsanity.com/paper/2110.08718/) (2021)
- [Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study](https://mlsanity.com/paper/2112.01925/) (2021)
- [Generation of the NIR spectral Band for Satellite Images with Convolutional Neural Networks](https://mlsanity.com/paper/2106.07020/) (2021)
- [Multi-scale Conditional Generative Modeling for Microscopic Image Restoration](https://mlsanity.com/paper/2407.05259/) (2024)
- [TUCaN: Progressively Teaching Colourisation to Capsules](https://mlsanity.com/paper/2106.15176/) (2021)
- [Bias in Automated Image Colorization: Metrics and Error Types](https://mlsanity.com/paper/2202.08143/) (2022)
- [Analysis of Different Losses for Deep Learning Image Colorization](https://mlsanity.com/paper/2204.02980/) (2022)
- [Collaboration among Image and Object Level Features for Image Colourisation](https://mlsanity.com/paper/2101.07576/) (2021)
- [Influence of Color Spaces for Deep Learning Image Colorization](https://mlsanity.com/paper/2204.02850/) (2022)
- [Deep Learning-Based MR Image Re-parameterization](https://mlsanity.com/paper/2206.05516/) (2022)
- [ParaColorizer: Realistic Image Colorization using Parallel Generative Networks](https://mlsanity.com/paper/2208.08295/) (2022)
- [UW-ProCCaps: UnderWater Progressive Colourisation with Capsules](https://mlsanity.com/paper/2307.01091/) (2023)
- [SPDGAN: A Generative Adversarial Network based on SPD Manifold Learning for Automatic Image Colorization](https://mlsanity.com/paper/2312.13506/) (2023)
- [Grayscale Image Colorization with GAN and CycleGAN in Different Image Domain](https://mlsanity.com/paper/2401.11425/) (2024)
- [Paired and Unpaired Image to Image Translation using Generative Adversarial Networks](https://mlsanity.com/paper/2505.16310/) (2025)
- [Automatic Image Colorization with Convolutional Neural Networks and Generative Adversarial Networks](https://mlsanity.com/paper/2508.05068/) (2025)
- [LoGoColor: Local-Global 3D Colorization for 360deg Scenes](https://mlsanity.com/paper/2512.09278/) (2025)
- [A Human-in-the-Loop Deep Learning Framework for Color Reconstruction of Lenticular Films](https://mlsanity.com/paper/2608.02835/) (2026)

## Abstract (arXiv)

Over the last decade, the process of automatic image colorization has been of significant interest for several application areas including restoration of aged or degraded images. This problem is highly ill-posed due to the large degrees of freedom during the assignment of color information. Many of the recent developments in automatic colorization involve images that contain a common theme or require highly processed data such as semantic maps as input. In our approach, we attempt to fully generalize the colorization procedure using a conditional Deep Convolutional Generative Adversarial Network (DCGAN), extend current methods to high-resolution images and suggest training strategies that speed up the process and greatly stabilize it. The network is trained over datasets that are publicly available such as CIFAR-10 and Places365. The results of the generative model and traditional deep neural networks are compared.