Open-AISP: An Open-Source AI Image Signal Processing Framework for Beginners

Open-AISP is an open-source AI-ISP framework designed specifically for beginners. It provides an end-to-end solution from raw image degradation simulation to neural network reconstruction and enhancement, covering core modules such as raw image simulation, joint denoising and demosaicing, helping beginners understand the principles and implementation of AI-ISP.

Traditional ISP relies on manual algorithms. While AI-ISP is on the rise, existing open-source projects are either complex or lack documentation. Open-AISP emerged to address this, with a modular design that provides a complete end-to-end solution, lowering the learning barrier for beginners.

Raw Image Simulation Module

• Reverse processing pipeline: Convert RGB images back to the raw domain, supporting multiple sensor formats
• Gaussian-Poisson noise model: Simulate camera shot/read noise
• PSF degradation: Simulate lens optical blur and chromatic aberration

Joint Denoising and Demosaicing Module (MF-JDD)

• Multi-frame denoising: Fuse multiple raw frames to improve signal-to-noise ratio
• Hardware noise estimation: Adaptive denoising based on gain calibration

Modules Under Development

MF-HDR synthesis, AITM tone mapping, DiffIPE diffusion enhancement

Raw simulation and MF-JDD modules have been completed:

• The Raw module supports sensor formats, noise modeling, and optical degradation
• MF-JDD, compared to OpenCV algorithms, preserves details and colors while denoising

The project uses Python3.11+ and MIT license, and is in the development stage.

Learning Value

Understand raw characteristics, deep learning applications, multi-frame fusion, and pipeline construction

Application Scenarios

Mobile photography enhancement, low-light recovery, computational photography research, CV preprocessing

Under development:

• Multi-frame alignment and registration
• MF-HDR synthesis
• AITM tone mapping
• DiffIPE diffusion enhancement Subsequent versions will be released gradually.

Open-AISP fills the gap in entry-level open-source tools. Its modular design and documentation provide a clear learning path, and it is expected to become an important resource for AI-ISP education and research.