Shine Stacker

Shine Stacker is a focus stacking application for microscopy, macro photography, and computational imaging. The software supports input in JPEG, 8 and 16-bit TIFF, PNG, and most RAW formats. Output can be JPEG, 8 and 16-bit TIFF, and PNG. It processes hundreds of images in batch: aligns, balances, and stacks layers. Its modular architecture allows combining processing modules according to project needs. Includes final interactive retouching of the stacked image from individual frames. For programmers, it offers a Python API and Jupyter Notebook support.

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• Image alignment. Corrects displacement between frames caused by vibrations or sample movement. The algorithm detects feature points and transforms each layer to spatially match the others, ensuring fine details overlap correctly.
• Exposure and color balancing. Normalizes brightness and white balance across different shots. Compensates for variations in microscope illumination or aperture changes, preventing tone differences in the final stack.
• In-focus region detection. Analyzes local sharpness at each pixel using wavelet transforms or gradients. Generates focus maps that determine which areas of each frame contribute to the merged result.
• Depth of field fusion. Selectively combines sharp zones from all images. The stacking engine blends pixels according to their focus level, producing a single shot with the entire scene sharp from front to back.
• Interactive layer retouching. Graphical interface displaying the full stack, allowing manual painting of areas from each frame for inclusion or exclusion. Users can correct artifacts or select specific regions with variable opacity brushes.
• Batch processing. Executes complete workflows on project folders. Reads predefined configurations and stacks hundreds of image series without manual intervention, ideal for large data volumes.
• Python API and scripting. Exposes all stacking functions as importable modules. Developers can build custom pipelines, automate repetitive tasks, and integrate Shine Stacker into existing acquisition systems.
• Jupyter Notebook support. Provides examples and specific functions for working within notebooks. Allows step-by-step visualization of alignment, focus maps, and fusion, facilitating experimentation and teaching.
• Modular module architecture. Decouples alignment, balancing, and stacking components. Users can swap implementations of each stage (e.g., using SIFT or ORB for alignment) based on their image characteristics.
• RAW format handling. Decodes camera and microscope raw files using integrated libraries. Preserves original bit depth and EXIF metadata, allowing work with maximum quality from the source.
• Depth mask generation. Creates relative depth maps from the focus sequence. These maps can be exported as 16-bit images for use in 3D modeling or topographic analysis.
• Saveable project profiles. Stores all processing options in configuration files. Profiles allow repeating the exact same workflow on different image series, ensuring reproducibility in research environments.

Shine Stacker development began in 2023. The application is written in Python, utilizing scientific libraries such as NumPy, SciPy, and OpenCV for image processing. The project is maintained by lucalista and other contributors on GitHub.

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