OpenFocus

OpenFocus delivers focus stacking quality that rivals commercial-grade software, while staying fully open source and easy to extend.

📢 News

Note

🎉 2026.01.13: Optimized ROI mode processing and fixed bugs to improve performance and stability.

🎉 2026.01.12: Added drag-and-drop image import on Mac and refactored core modules for improved code maintainability and readability.

🎉 2026.01.10: Added bilingual support, a status dashboard, and new ROI fusion options to improve efficiency and flexibility.

🎉 2026.01.09: Improved UI and navigation, faster parallel processing, multi-folder batch support, and bug fixes.

🎉 2025.12.11: Added functionality to read image stacks in video format.

🎉 2025.12.11: Thanks to Rangj for providing the C++ implementation of the GFG-FGF fusion algorithm, which is now available in the software.

🎉 2025.12.11: We have fixed some bugs and added configuration options such as block-wise fusion to avoid OOM (Out of Memory) issues.

🎉 2025.12.05: OpenFocus officially released — welcome to try it.

⚙️ Environment Setup

conda create -n openfocus python=3.10
conda activate openfocus
pip install opencv-python pyqt6 numpy imageio dtcwt scipy torch torchvision 
python main.py

Pre-built package (Windows only): Grab the compact Windows build from the Releases page; other platforms can run from source.

Table of Contents

• ⚙️ Environment Setup
• 🔭 Overview
• ✨ Highlights
• 🧪 Fusion & Registration Methods
• 📚 References
• 🤝 Contribution
• 📄 License

🔭 Overview

OpenFocus is a PyQt6-based multi-focus registration and fusion workstation that delivers commercial-grade alignment and blending results. The project is fully open source (MIT License) and runs on CPU by default with optional GPU acceleration for the StackMFF V4 neural model.

✨ Highlights

• Beginner-Friendly: Plug-and-play workflows with unapologetically simple, guided operations.
• Flexible Processing Flows: Run fusion-only, registration-only, or combined registration + fusion pipelines depending on your workload.
• Batch Automation: Kick off batch jobs across multiple folders with live progress, cancellation, and automatic output organization.
• Annotation & Export Toolkit: Overlay labels, export GIF animations, and save processed stacks in JPG/PNG/BMP/TIFF with consistent metadata handling.
• AI-Assisted Fusion: Ship with StackMFF V4 to unlock deep-learning-quality fusion alongside classic signal-processing methods.

🧪 Algorithms

Fusion Algorithms

• Guided Filter: Fast edge-preserving fusion that enhances contrast while suppressing noise.
• DCT Multi-Focus Fusion: Frequency-domain technique optimized for crisp detail recovery.
• Dual-Tree Complex Wavelet Transform (DTCWT): Multi-scale representation that preserves fine texture structures.
• GFG-FGF: GFG-FGF is based on a generalized four-neighborhood Gaussian gradient (GFG) operator combined with a fast guided filter (FGF).
• StackMFF V4: Pretrained deep model delivering state-of-the-art focus stacking quality.

Registration Algorithms

• Homography: Performs feature-based projective alignment using keypoint matching and RANSAC to handle global perspective transformations.
• ECC: Performs intensity-based alignment by maximizing the enhanced correlation coefficient for precise, sub-pixel registration.

License Notice: Every fusion/registration algorithm included comes from open-source research implementations. When using or redistributing them, please follow each algorithm’s original license terms in addition to the OpenFocus MIT license.

📚 References

• M. B. A. Haghighat, A. Aghagolzadeh, and H. Seyedarabi, "Multi-focus image fusion for visual sensor networks in DCT domain," Computers & Electrical Engineering, vol. 37, no. 5, pp. 789-797, 2011.
• J. J. Lewis, R. J. O'Callaghan, S. G. Nikolov, D. R. Bull, and N. Canagarajah, "Pixel- and region-based image fusion with complex wavelets," Information Fusion, vol. 8, no. 2, pp. 119-130, 2007.
• S. Li, X. Kang, and J. Hu, "Image fusion with guided filtering," IEEE Transactions on Image Processing, vol. 22, no. 7, pp. 2864-2875, 2013.
• 付宏语, 巩岩, 汪路涵, 等. 多聚焦显微图像融合算法[J]. Laser & Optoelectronics Progress, 2024, 61(6): 0618022-0618022-9.

🤝 Contribution

We welcome community contributions of all kinds:

1. Issues: Report bugs, request features, or propose UX enhancements.
2. Algorithm & Performance Work: Share new fusion/registration ideas, optimizations.

Bug reports or suggestions? Please open an issue so we can follow up quickly.

📄 License

This project is released under the MIT License. Feel free to use, modify, and distribute within the terms of the license.

If you publish images created with OpenFocus, please consider adding a note such as:

Created with OpenFocus – https://github.com/Xinzhe99/OpenFocus

This is not mandatory, but highly appreciated.

If OpenFocus helps you, please consider leaving a ⭐ on the repository!