A Human-in-the-Loop Deep Learning Framework for Color Reconstruction of Lenticular Films
Quick Answer
The proposed human-in-the-loop (HITL) deep learning framework enhances color reconstruction of lenticular films by allowing expert refinement of boundary positions, improving robustness in challenging cases.
Quick Take
This method successfully merges reconstructed chrominance with original luminance, yielding high-quality, exhibitable results where previous automated techniques failed.
Key Points
- Introduces editable, vector-based representations for lenticule boundaries.
- Improves color reconstruction in challenging cases like curved lenticules.
- Successfully merges reconstructed chrominance with original film luminance.
- First to integrate expert guidance with texture-preserving post-processing.
- Achieves high-quality results suitable for exhibition, unlike previous methods.
DeepSignal Analysis
What happened
A new human-in-the-loop (HITL) deep learning framework has been proposed for color reconstruction of lenticular films. This method allows experts to refine boundary positions interactively, which enhances the accuracy of color extraction and demosaicing processes. The framework merges reconstructed chrominance with original luminance, achieving high-quality results in challenging cases where previous automated methods struggled.
Key evidence
- The HITL framework introduces an editable, vector-based representation of lenticule boundaries, enabling expert interaction before color extraction.
- Previous automated techniques, such as doLCE and deep-doLCE, often fail with curved lenticules and low-contrast regions, highlighting the need for this new approach.
- The proposed method successfully produces exhibitable color reconstructions, preserving texture, in cases where earlier methods yielded unsatisfactory results.
Why it matters
This development is significant as it addresses the limitations of existing automated techniques in color reconstruction of lenticular films. By incorporating expert knowledge into the process, the HITL framework enhances the robustness and quality of the output, making it suitable for exhibition. This advancement could have implications for the preservation of historical films and the quality of digital restorations.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Historical lenticular films, such as those created with the Kodacolor process, encode color information in a distinctive spatial format. This structure requires specialized techniques for accurate color reconstruction. While recent signal processing approaches like doLCE and deep learning methods like deep-doLCE have advanced automated color recovery, they often fail with cases such as curved lenticules, low-contrast, or badly captured regions. We propose a human-in-the-loop (HITL) deep learning framework which is designed for color reconstruction in lenticular films. Our approach introduces an editable, vector-based representation of lenticule boundaries, allowing experts to interactively refine boundary positions before color extraction and demosaicing. This decoupled architecture enables targeted corrections and iterative fine-tuning, embedding expert knowledge into the detection model and improving robustness across challenging frames. To preserve image details using information solely present in the original silver emulsion, we merge the reconstructed chrominance with the original film scan's luminance. We evaluate our pipeline on a challenging lenticular film sequence where previous automated approaches fail and the reconstructed colors are not suitable for exhibition. In contrast, our HITL approach successfully produces high-quality, exhibitable color reconstructions with preserved texture. This work is the first to combine expert guidance, editable intermediate representations, and texture-preserving post-processing for lenticular film color reconstruction, advancing the state of the art in this field.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2608.02835 [cs.CV] |
| (or arXiv:2608.02835v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2608.02835 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Saptarshi Neil Sinha [view email]
[v1]
Mon, 3 Aug 2026 19:48:48 UTC (2,092 KB)
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CV
See more →ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities
ProMoE-FL introduces a Prototype-conditioned Mixture-of-Experts framework for multimodal federated learning, effectively addressing missing modalities. It outperforms existing methods on four chest X-ray datasets, demonstrating superior feature synthesis capabilities in both homogeneous and heterogeneous settings.