Open-AISP provides a basic, open-source framework for building AI-driven image signal processing (ISP) pipelines. It simulates the full workflow of processing raw camera data into enhanced images, following standard industry steps from degradation to reconstruction. Designed as a toy-level project, it targets beginners who want to experiment with AI-ISP concepts without complex setups. The repository, hosted at https://github.com/coolsyn2000/Open-AISP, uses Python (3.11+) and holds 20 GitHub stars. Its MIT license allows free use and modification.
The pipeline starts with realistic raw image degradation and moves to neural enhancement. This mirrors real camera pipelines, where sensors capture noisy raw data that requires denoising, demosaicing, HDR merging, tonemapping, and post-processing. Open-AISP fills a gap for educational tools: most ISP research uses proprietary datasets or hardware-specific code, but this offers an accessible entry point with simulated data from public sources like DIV2K and Flickr2K.
Core modules
Open-AISP breaks its pipeline into distinct modules, with two implemented and others planned.
The raw-sim module generates degraded raw data mimicking real sensors. It reverses high-quality sRGB images into the raw domain, supporting 4x4 quadbayer and 2x2 binning color filter formats. Noise simulation uses a Gaussian-Poisson mixed model, calibrated for shot noise (from photoelectric conversion) and read noise. For example, it processes DIV2K inputs at ISO 6400 to produce noisy raw outputs, as shown in sample images from the README. Optical PSF degradation for lens blur and chromatic aberration remains a TODO.
Next, the MF-JDD (Joint Denoising and Demosaicing) module reconstructs linear RGB images from multi-frame raw inputs. It estimates hardware noise maps based on analog/digital gain and calibration parameters, then applies deep learning to denoise and demosaic. Comparisons in the README pit its output against OpenCV demosaicing and ground truth, showing improved detail retention. Burst image alignment is WIP.
Future modules include:
- Multi-frame HDR synthesis from EV0/EV-/EV-- exposures.
- Learning-based tonemapping (AITM) for linear RGB to sRGB.
- Diffusion-based post-enhancement (DiffIPE) using pre-trained models.
These align with the project roadmap, where raw-sim and JDD basics are complete, but alignment and HDR work remain pending.
Project timeline and status
Development began on April 25, 2026, with the repo init including raw-sim and JDD. Two days later, bilingual MkDocs documentation launched—English at https://www.coolsyn.top/Open-AISP/ and Chinese at https://www.coolsyn.top/Open-AISP/zh/. Badges confirm active status, Python 3.11+ requirement, and MIT license.
The roadmap checklist details progress:
- Raw-sim: Unprocess pipeline, sensor formats, noise modeling checked off; PSF as open item.
- JDD: Architecture, multi-frame fusion, noise estimation done; alignment WIP.
- HDR, tonemapping, and enhancement modules are in planning (⏳).
This phased approach lets users test early components while modules mature.
Getting it running
As an early-stage project, Open-AISP lacks a one-click installer like pip or Docker in the provided README. Start by cloning the repository:
git clone https://github.com/coolsyn2000/Open-AISP.git
cd Open-AISP
It requires Python 3.11 or higher. Set up a virtual environment and install dependencies manually—likely PyTorch or similar for neural modules, though exact requirements appear in the docs or module folders like ./raw-sim/ and ./JDD/. Run raw-sim examples by loading DIV2K/Flickr2K images and applying the unprocess pipeline for noisy raw outputs. For JDD, prepare multi-frame raw inputs and execute the model inference, comparing against OpenCV baselines as in the assets.
Samples include input sRGB to output raw visuals in ./raw-sim/assets/, and demosaicing comparisons in ./JDD/assets/. Full usage details, including training scripts, live in the bilingual documentation sites. Test on a machine with GPU acceleration for JDD, given its deep learning components.
Who this is for
Open-AISP suits beginners in computer vision or photography pipelines seeking hands-on AI-ISP experience. Students or hobbyists can simulate raw degradation without real camera hardware, using public datasets to train models on denoising or demosaicing. Researchers prototyping toy pipelines find value in its modular design—extend raw-sim for custom noise or integrate JDD into larger ISP chains.
Use cases include educational demos: generate ISO 6400 raw from clean sRGB, then reconstruct with JDD to study noise impacts. It's not tuned for production photography apps, but helps understand why industry ISPs use multi-frame fusion or calibrated noise models. If you experiment with burst processing or sensor simulation, the code paths like ./raw-sim/ provide concrete starting points.
How it compares
As a toy-level framework, Open-AISP contrasts with mature alternatives like RawNeRF or ISPNet, which offer production-ready raw-to-RGB but demand more setup and data. Those handle full pipelines with pre-trained weights; Open-AISP focuses on simulation and basics, weighing less on resources but lacking polish. For raw simulation alone, tools like LibRaw or dcraw provide C-based unprocessing without AI, missing the Gaussian-Poisson noise or quadbayer support here.
JDD resembles multi-frame denoisers in PyTorch hubs (e.g., burst-SR models), but integrates demosaicing and noise estimation natively. Heavier frameworks like Adobe's RAW processor or DeepISP use proprietary stacks; this open version runs locally with Python scripts. At 20 stars, it's lighter than repos like Real-ESRGAN (20k+ stars), trading completeness for simplicity.
Current limitations and resources
Several features remain TODO or WIP, like PSF degradation, burst alignment, and HDR modules, so the pipeline isn't end-to-end yet. Production users needing robust, trained models should look elsewhere—Open-AISP prioritizes learning over deployment. Production users or those without Python experience might skip it.
Track updates via the GitHub repo or docs. Samples and assets illustrate outputs directly.
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