# Image Colorization By Capsule Networks

arXiv: https://arxiv.org/abs/1908.08307 · HTML: https://mlsanity.com/paper/1908.08307/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2019.

## Built on by (most cited first, 10 of 10)

- [Color Equivariant Convolutional Networks](https://mlsanity.com/paper/2310.19368/) (2023)
- [ColorFLUX: A Structure-Color Decoupling Framework for Old Photo Colorization](https://mlsanity.com/paper/2603.28162/) (2026)
- [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)
- [CCC: Color Classified Colorization](https://mlsanity.com/paper/2403.01476/) (2024)
- [Collaboration among Image and Object Level Features for Image Colourisation](https://mlsanity.com/paper/2101.07576/) (2021)
- [Human vs Objective Evaluation of Colourisation Performance](https://mlsanity.com/paper/2204.05200/) (2022)
- [UW-ProCCaps: UnderWater Progressive Colourisation with Capsules](https://mlsanity.com/paper/2307.01091/) (2023)
- [CCC++: Optimized Color Classified Colorization with Segment Anything Model (SAM) Empowered Object Selective Color Harmonization](https://mlsanity.com/paper/2403.11494/) (2024)
- [Computer-aided Colorization State-of-the-science: A Survey](https://mlsanity.com/paper/2410.02288/) (2024)

## Abstract (arXiv)

In this paper, a simple topology of Capsule Network (CapsNet) is investigated for the problem of image colorization. The generative and segmentation capabilities of the original CapsNet topology, which is proposed for image classification problem, is leveraged for the colorization of the images by modifying the network as follows:1) The original CapsNet model is adapted to map the grayscale input to the output in the CIE Lab colorspace, 2) The feature detector part of the model is updated by using deeper feature layers inherited from VGG-19 pre-trained model with weights in order to transfer low-level image representation capability to this model, 3) The margin loss function is modified as Mean Squared Error (MSE) loss to minimize the image-to-imagemapping. The resulting CapsNet model is named as Colorizer Capsule Network (ColorCapsNet).The performance of the ColorCapsNet is evaluated on the DIV2K dataset and promising results are obtained to investigate Capsule Networks further for image colorization problem.