# Image Colorization using U-Net with Skip Connections and Fusion Layer on Landscape Images

arXiv: https://arxiv.org/abs/2205.12867 · HTML: https://mlsanity.com/paper/2205.12867/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 25 May 2022.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Real-Time User-Guided Image Colorization with Learned Deep Priors](https://mlsanity.com/paper/1705.02999/) (2017)
- [Deep Koalarization: Image Colorization using CNNs and Inception-ResNet-v2](https://mlsanity.com/paper/1712.03400/) (2017)
- [U-Net with Graph Based Smoothing Regularizer for Small Vessel Segmentation on Fundus Image](https://mlsanity.com/paper/2009.07567/) (2020)

## Abstract (arXiv)

We present a novel technique to automatically colorize grayscale images that combine the U-Net model and Fusion Layer features. This approach allows the model to learn the colorization of images from pre-trained U-Net. Moreover, the Fusion layer is applied to merge local information results dependent on small image patches with global priors of an entire image on each class, forming visually more compelling colorization results. Finally, we validate our approach with a user study evaluation and compare it against state-of-the-art, resulting in improvements.