Modular Neural Image Signal Processing
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
6938fc77dfc35938ba12a12c
Mahmoud Afifi
claimed_verified
2025-12-10T08:39:32.140Z
6574973c05e573071548922c
https://cdn-avatars.huggingface.co/v1/production/uploads/6574973c05e573071548922c/xGg83Tq-nCT6mH2mkiIv6.png
Mahmoud Afifi
mafifi
user
mafifi
6938fc77dfc35938ba12a12d
Zhongling Wang
6938fc77dfc35938ba12a12e
Ran Zhang
6938fc77dfc35938ba12a12f
Michael S. Brown
Modular Neural Image Signal Processing
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
A modular neural ISP framework provides high rendering accuracy, scalability, and flexibility for diverse photo-editing operations with competitive results.
neural ISP modular design rendering accuracy scalability debuggability generalization user-preference styles photo-editing tool post-editable re-rendering