# Cooperative Colorization: Exploring Latent Cross-Domain Priors for NIR Image Spectrum Translation

arXiv: https://arxiv.org/abs/2308.03348 · HTML: https://mlsanity.com/paper/2308.03348/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 11 of 11)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation](https://mlsanity.com/paper/2003.13659/) (2020)
- [GAN Prior Embedded Network for Blind Face Restoration in the Wild](https://mlsanity.com/paper/2105.06070/) (2021)
- [Neural Volumes: Learning Dynamic Renderable Volumes from Images](https://mlsanity.com/paper/1906.07751/) (2019)
- [Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy](https://mlsanity.com/paper/1912.07726/) (2019)
- [Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation](https://mlsanity.com/paper/2003.00867/) (2020)
- [Colorization Transformer](https://mlsanity.com/paper/2102.04432/) (2021)
- [Instance-aware Image Colorization](https://mlsanity.com/paper/2005.10825/) (2020)
- [Towards Vivid and Diverse Image Colorization with Generative Color Prior](https://mlsanity.com/paper/2108.08826/) (2021)
- [Coloring With Limited Data: Few-Shot Colorization via Memory-Augmented Networks](https://mlsanity.com/paper/1906.11888/) (2019)

## Built on by (most cited first, 6 of 6)

- [Any2Any: Unified Arbitrary Modality Translation for Remote Sensing](https://mlsanity.com/paper/2603.04114/) (2026)
- [Multi-scale HSV Color Feature Embedding for High-fidelity NIR-to-RGB Spectrum Translation](https://mlsanity.com/paper/2404.16685/) (2024)
- [ColorMamba: Towards High-quality NIR-to-RGB Spectral Translation with Mamba](https://mlsanity.com/paper/2408.08087/) (2024)
- [DeepSPG: Exploring Deep Semantic Prior Guidance for Low-light Image Enhancement with Multimodal Learning](https://mlsanity.com/paper/2504.19127/) (2025)
- [Rethinking Occlusion in FER: A Semantic-Aware Perspective and Go Beyond](https://mlsanity.com/paper/2507.15401/) (2025)
- [Histogram Assisted Quality Aware Generative Model for Resolution Invariant NIR Image Colorization](https://mlsanity.com/paper/2601.01103/) (2026)

## Abstract (arXiv)

Near-infrared (NIR) image spectrum translation is a challenging problem with many promising applications. Existing methods struggle with the mapping ambiguity between the NIR and the RGB domains, and generalize poorly due to the limitations of models' learning capabilities and the unavailability of sufficient NIR-RGB image pairs for training. To address these challenges, we propose a cooperative learning paradigm that colorizes NIR images in parallel with another proxy grayscale colorization task by exploring latent cross-domain priors (i.e., latent spectrum context priors and task domain priors), dubbed CoColor. The complementary statistical and semantic spectrum information from these two task domains -- in the forms of pre-trained colorization networks -- are brought in as task domain priors. A bilateral domain translation module is subsequently designed, in which intermittent NIR images are generated from grayscale and colorized in parallel with authentic NIR images; and vice versa for the grayscale images. These intermittent transformations act as latent spectrum context priors for efficient domain knowledge exchange. We progressively fine-tune and fuse these modules with a series of pixel-level and feature-level consistency constraints. Experiments show that our proposed cooperative learning framework produces satisfactory spectrum translation outputs with diverse colors and rich textures, and outperforms state-of-the-art counterparts by 3.95dB and 4.66dB in terms of PNSR for the NIR and grayscale colorization tasks, respectively.