Modular Neural Image Signal Processing
Mahmoud Afifi, Zhongling Wang, Ran Zhang, Michael S. Brown
2025-12-10
Summary
This paper introduces a new way to process images using artificial intelligence, specifically focusing on improving how raw image data from a camera is turned into a beautiful picture you see on a screen.
What's the problem?
Traditionally, turning raw camera data into a viewable image involves a lot of complicated steps, and existing AI-powered methods aren't very flexible or easy to adjust. They often treat the whole process as one big block, making it hard to fine-tune specific aspects of the image or adapt to different camera types and personal preferences.
What's the solution?
The researchers created a system that breaks down image processing into smaller, independent modules. Think of it like building with LEGOs – each module handles a specific task, like adjusting color or reducing noise. This modular design allows for greater control, easier debugging, and the ability to customize the image processing pipeline for different cameras and desired styles. They even built a photo editing tool to show how well it works, allowing users to easily experiment with different looks.
Why it matters?
This research is important because it makes AI-powered image processing more adaptable and user-friendly. The modular approach means the system can be improved more easily, work with a wider range of cameras, and allow users to create images that perfectly match their vision, all while keeping the system relatively small and efficient in terms of computing power.
Abstract
This paper presents a modular neural image signal processing (ISP) framework that processes raw inputs and renders high-quality display-referred images. Unlike prior neural ISP designs, our method introduces a high degree of modularity, providing full control over multiple intermediate stages of the rendering process.~This modular design not only achieves high rendering accuracy but also improves scalability, debuggability, generalization to unseen cameras, and flexibility to match different user-preference styles. To demonstrate the advantages of this design, we built a user-interactive photo-editing tool that leverages our neural ISP to support diverse editing operations and picture styles. The tool is carefully engineered to take advantage of the high-quality rendering of our neural ISP and to enable unlimited post-editable re-rendering. Our method is a fully learning-based framework with variants of different capacities, all of moderate size (ranging from ~0.5 M to ~3.9 M parameters for the entire pipeline), and consistently delivers competitive qualitative and quantitative results across multiple test sets. Watch the supplemental video at: https://youtu.be/ByhQjQSjxVM