Aduku Stacker vs ChimpStackr

Aduku Stacker combines photographs taken at different focus distances into a sharp image, for macro, product, landscape and microscopy photography. The free app offers five merge methods: Wavelet, Pyramid, Weighted, Hybrid and Depthmap. Its default workflow processes images at 16-bit and uses 32-bit floating-point merge calculations, with memory-bounded batching for deep stacks. Automatic alignment uses feature matching and subpixel refinement; options include focus-breathing correction, homography, mesh warp and manual alignment seeding. It imports JPEG, PNG, TIFF, BMP, HEIC and DNG, and exports JPEG, PNG, TIFF, linear DNG and depth maps. Proprietary RAW files may require Adobe DNG Converter on macOS. Retouching lets users paint from aligned source frames, erase strokes and undo or redo, but there is no clone tool. Processing is local and does not require an internet connection. It runs on macOS 13 or later and 64-bit Windows 10/11; 8 GB RAM is sufficient for most stacks, with 16 GB recommended for deep 16-bit RAW stacks.

ChimpStackr is a free, open-source application for focus stacking, with graphical and headless command-line interfaces. It offers Laplacian Pyramid, Weighted Average, Depth Map, and Exposure Fusion methods. Automatic alignment supports translation, Euclidean rotation, similarity rotation and scale, and affine correction. Its 16-bit pipeline preserves bit depth from RAW input to output, and automatic cropping removes black edges caused by alignment shifts. Users can drag image files or folders into the app, compare input and output with a slider, and pause, resume, or cancel long-running stacks. Auto-tuning detects parameters from image resolution. Downloads are listed for Windows as a ZIP, macOS as a DMG, and Linux x86-64 as an AppImage; TIFF and PNG are listed output formats, and RAW files are supported. The project uses the GPL-3.0 license. Exposure Fusion is described for images with varying exposure or lighting, rather than focus stacking. Building from source requires Python 3.9–3.13, and builds can only be made for the current platform.