# Two Decades of Colorization and Decolorization for Images and Videos

arXiv: https://arxiv.org/abs/2204.13322 · HTML: https://mlsanity.com/paper/2204.13322/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 97 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 17 of 17)

- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [A Learned Representation For Artistic Style](https://mlsanity.com/paper/1610.07629/) (2016)
- [Learning Representations for Automatic Colorization](https://mlsanity.com/paper/1603.06668/) (2016)
- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [Tracking Emerges by Colorizing Videos](https://mlsanity.com/paper/1806.09594/) (2018)
- [Real-Time User-Guided Image Colorization with Learned Deep Priors](https://mlsanity.com/paper/1705.02999/) (2017)
- [Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration](https://mlsanity.com/paper/1805.03857/) (2018)
- [Exploring the structure of a real-time, arbitrary neural artistic stylization network](https://mlsanity.com/paper/1705.06830/) (2017)
- [Deep Exemplar-based Colorization](https://mlsanity.com/paper/1807.06587/) (2018)
- [Deep Colorization](https://mlsanity.com/paper/1605.00075/) (2016)
- [Video Propagation Networks](https://mlsanity.com/paper/1612.05478/) (2016)
- [Learning Diverse Image Colorization](https://mlsanity.com/paper/1612.01958/) (2016)
- [Fully Automatic Video Colorization with Self-Regularization and Diversity](https://mlsanity.com/paper/1908.01311/) (2019)
- [Characterizing and Improving Stability in Neural Style Transfer](https://mlsanity.com/paper/1705.02092/) (2017)
- [Deep Video Color Propagation](https://mlsanity.com/paper/1808.03232/) (2018)
- [Switchable Temporal Propagation Network](https://mlsanity.com/paper/1804.08758/) (2018)
- [Deep Feature Consistent Deep Image Transformations: Downscaling, Decolorization and HDR Tone Mapping](https://mlsanity.com/paper/1707.09482/) (2017)

## Built on by (most cited first, 2 of 2)

- [Equalization and Brightness Mapping Modes of Color-to-Gray Projection Operators](https://mlsanity.com/paper/2208.09950/) (2022)
- [A Psychological Study: Importance of Contrast and Luminance in Color to Grayscale Mapping](https://mlsanity.com/paper/2402.04583/) (2024)

## Abstract (arXiv)

Colorization is a computer-aided process, which aims to give color to a gray image or video. It can be used to enhance black-and-white images, including black-and-white photos, old-fashioned films, and scientific imaging results. On the contrary, decolorization is to convert a color image or video into a grayscale one. A grayscale image or video refers to an image or video with only brightness information without color information. It is the basis of some downstream image processing applications such as pattern recognition, image segmentation, and image enhancement. Different from image decolorization, video decolorization should not only consider the image contrast preservation in each video frame, but also respect the temporal and spatial consistency between video frames. Researchers were devoted to develop decolorization methods by balancing spatial-temporal consistency and algorithm efficiency. With the prevalance of the digital cameras and mobile phones, image and video colorization and decolorization have been paid more and more attention by researchers. This paper gives an overview of the progress of image and video colorization and decolorization methods in the last two decades.