| # VAEEDOF - High-Resolution Multi-Focus Image Fusion | |
| ## Model Description | |
| VAEEDOF is a deep learning model designed to address the Depth-of-Field (DOF) constraint in photography using Multi-Focus Image Fusion (MFIF). Built upon a distilled Variational Autoencoder (VAE) architecture, this model fuses up to 7 images with different focus points into a single, high-resolution, all-in-focus image. | |
| It is trained to produce artifact-free and photorealistic fused outputs and demonstrates strong generalization across both synthetic and real-world datasets. | |
| ## 📦 Model Weights | |
| This repository provides: | |
| - ✅ Pretrained VAEEDOF weights used in our experiments | |
| - 🔁 Comparison model weights for evaluating against other state-of-the-art methods (baselines) | |
| ## 🧪 Training Data | |
| The model is trained on the MattingMFIF dataset – a new, high-quality 4K synthetic dataset built using matting techniques applied to real-world photographs to simulate realistic depth-of-field blur and focus patterns. | |
| ## 🔗 Resources | |
| GitHub Repository (Code, training & inference scripts): | |
| 👉 https://github.com/MalumaDev/VAEEDOF | |
| ## 📄 Citation | |
| ``` | |
| @article{piano2025addressing, | |
| title={Addressing the Depth-of-Field Constraint: A New Paradigm for High Resolution Multi-Focus Image Fusion}, | |
| author={Piano, Luca and Huanwen, Peng and Bilcu, Radu Ciprian}, | |
| journal={arXiv preprint arXiv:2510.19581}, | |
| year={2025} | |
| } | |
| ``` |