# Cross-Camera Deep Colorization

arXiv: https://arxiv.org/abs/2209.01211 · HTML: https://mlsanity.com/paper/2209.01211/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 62 references that are in the map.
- First posted on arXiv 26 Aug 2022.

## Builds on (most cited first, 21 of 21)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [Visual Attribute Transfer through Deep Image Analogy](https://mlsanity.com/paper/1705.01088/) (2017)
- [CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping](https://mlsanity.com/paper/1807.10547/) (2018)
- [Deep Exemplar-based Colorization](https://mlsanity.com/paper/1807.06587/) (2018)
- [Deep Colorization](https://mlsanity.com/paper/1605.00075/) (2016)
- [Learning to Synthesize a 4D RGBD Light Field from a Single Image](https://mlsanity.com/paper/1708.03292/) (2017)
- [PANDA: A Gigapixel-level Human-centric Video Dataset](https://mlsanity.com/paper/2003.04852/) (2020)
- [Coloring With Limited Data: Few-Shot Colorization via Memory-Augmented Networks](https://mlsanity.com/paper/1906.11888/) (2019)
- [Unsupervised Diverse Colorization via Generative Adversarial Networks](https://mlsanity.com/paper/1702.06674/) (2017)
- [Light Field Video Capture Using a Learning-Based Hybrid Imaging System](https://mlsanity.com/paper/1705.02997/) (2017)
- [Progressive Color Transfer with Dense Semantic Correspondences](https://mlsanity.com/paper/1710.00756/) (2017)
- [EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation](https://mlsanity.com/paper/2110.07797/) (2021)
- [Zoom in to the details of human-centric videos](https://mlsanity.com/paper/2005.13222/) (2020)
- [MANet: Improving Video Denoising with a Multi-Alignment Network](https://mlsanity.com/paper/2202.09704/) (2022)
- [Cross-Camera Human Motion Transfer by Time Series Analysis](https://mlsanity.com/paper/2109.14174/) (2021)

## Built on by (most cited first, 1 of 1)

- [Improving Video Colorization by Test-Time Tuning](https://mlsanity.com/paper/2307.11757/) (2023)

## Abstract (arXiv)

In this paper, we consider the color-plus-mono dual-camera system and propose an end-to-end convolutional neural network to align and fuse images from it in an efficient and cost-effective way. Our method takes cross-domain and cross-scale images as input, and consequently synthesizes HR colorization results to facilitate the trade-off between spatial-temporal resolution and color depth in the single-camera imaging system. In contrast to the previous colorization methods, ours can adapt to color and monochrome cameras with distinctive spatial-temporal resolutions, rendering the flexibility and robustness in practical applications. The key ingredient of our method is a cross-camera alignment module that generates multi-scale correspondences for cross-domain image alignment. Through extensive experiments on various datasets and multiple settings, we validate the flexibility and effectiveness of our approach. Remarkably, our method consistently achieves substantial improvements, i.e., around 10dB PSNR gain, upon the state-of-the-art methods. Code is at: https://github.com/IndigoPurple/CCDC