# Perceptual Region-Driven Infrared-Visible Co-Fusion for Extreme Scene Enhancement

arXiv: https://arxiv.org/abs/2512.06400 · HTML: https://mlsanity.com/paper/2512.06400/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 59 references that are in the map.
- First posted on arXiv 6 Dec 2025.

## Builds on (most cited first, 9 of 9)

- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [LLVIP: A Visible-infrared Paired Dataset for Low-light Vision](https://mlsanity.com/paper/2108.10831/) (2021)
- [CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion](https://mlsanity.com/paper/2211.14461/) (2022)
- [Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation](https://mlsanity.com/paper/2003.08073/) (2020)
- [MDLatLRR: A novel decomposition method for infrared and visible image fusion](https://mlsanity.com/paper/1811.02291/) (2018)
- [Scene Segmentation-Based Luminance Adjustment for Multi-Exposure Image Fusion](https://mlsanity.com/paper/1903.07428/) (2019)
- [Infrared and Visible Image Fusion with Language-Driven Loss in CLIP Embedding Space](https://mlsanity.com/paper/2402.16267/) (2024)
- [Event-based multi-view photogrammetry for high-dynamic, high-velocity target measurement](https://mlsanity.com/paper/2506.00578/) (2025)
- [Six-Point Method for Multi-Camera Systems with Reduced Solution Space](https://mlsanity.com/paper/2402.18066/) (2024)

## Built on by (most cited first, 4 of 4)

- [A DMD-Based Adaptive Modulation Method for High Dynamic Range Imaging in High-Glare Environments](https://mlsanity.com/paper/2602.12044/) (2026)
- [Learning to reconstruct from saturated data: audio declipping and high-dynamic range imaging](https://mlsanity.com/paper/2602.22279/) (2026)
- [High Dynamic Range Imaging Based on an Asymmetric Event-SVE Camera System](https://mlsanity.com/paper/2603.00467/) (2026)
- [Synergistic Event-SVE Imaging for Quantitative Propellant Combustion Diagnostics](https://mlsanity.com/paper/2603.25054/) (2026)

## Abstract (arXiv)

In photogrammetry, accurately fusing infrared (IR) and visible (VIS) spectra while preserving the geometric fidelity of visible features and incorporating thermal radiation is a significant challenge, particularly under extreme conditions. Existing methods often compromise visible imagery quality, impacting measurement accuracy. To solve this, we propose a region perception-based fusion framework that combines multi-exposure and multi-modal imaging using a spatially varying exposure (SVE) camera. This framework co-fuses multi-modal and multi-exposure data, overcoming single-exposure method limitations in extreme environments. The framework begins with region perception-based feature fusion to ensure precise multi-modal registration, followed by adaptive fusion with contrast enhancement. A structural similarity compensation mechanism, guided by regional saliency maps, optimizes IR-VIS spectral integration. Moreover, the framework adapts to single-exposure scenarios for robust fusion across different conditions. Experiments conducted on both synthetic and real-world data demonstrate superior image clarity and improved performance compared to state-of-the-art methods, as evidenced by both quantitative and visual evaluations.