# Neural Restoration of Greening Defects in Historical Autochrome Photographs Based on Purely Synthetic Data

arXiv: https://arxiv.org/abs/2505.22291 · HTML: https://mlsanity.com/paper/2505.22291/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 12 of 12)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [Benchmarking Single Image Dehazing and Beyond](https://mlsanity.com/paper/1712.04143/) (2017)
- [InstructIR: High-Quality Image Restoration Following Human Instructions](https://mlsanity.com/paper/2401.16468/) (2024)
- [ChromaGAN: Adversarial Picture Colorization with Semantic Class Distribution](https://mlsanity.com/paper/1907.09837/) (2019)
- [Deep White-Balance Editing](https://mlsanity.com/paper/2004.01354/) (2020)
- [CM-GAN: Image Inpainting with Cascaded Modulation GAN and Object-Aware Training](https://mlsanity.com/paper/2203.11947/) (2022)
- [Synthetic Data in AI: Challenges, Applications, and Ethical Implications](https://mlsanity.com/paper/2401.01629/) (2024)
- [Semantic White Balance: Semantic Color Constancy Using Convolutional Neural Network](https://mlsanity.com/paper/1802.00153/) (2018)
- [Strong and Controllable Blind Image Decomposition](https://mlsanity.com/paper/2403.10520/) (2024)
- [Finlay, Thames, Dufay, and Paget color screen process collections: Using digital registration of viewing screens to reveal original color](https://mlsanity.com/paper/2211.16076/) (2022)

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

- [Curating Synthetic Data for Task-Specific Visual Perception](https://mlsanity.com/paper/2609.38476/) (2026)

## Abstract (arXiv)

The preservation of early visual arts, particularly color photographs, is challenged by deterioration caused by aging and improper storage, leading to issues like blurring, scratches, color bleeding, and fading defects. Despite great advances in image restoration and enhancement in recent years, such systematic defects often cannot be restored by current state-of-the-art software features as available e.g. in Adobe Photoshop, but would require the incorporation of defect-aware priors into the underlying machine learning techniques. However, there are no publicly available datasets of autochromes with defect annotations. In this paper, we address these limitations and present the first approach that allows the automatic removal of greening color defects in digitized autochrome photographs. For this purpose, we introduce an approach for accurately simulating respective defects and use the respectively obtained synthesized data with its ground truth defect annotations to train a generative AI model with a carefully designed loss function that accounts for color imbalances between defected and non-defected areas. As demonstrated in our evaluation, our approach allows for the efficient and effective restoration of the considered defects, thereby overcoming limitations of alternative techniques that struggle with accurately reproducing original colors and may require significant manual effort.