Mahmoud Afifi
Abstract
A modular neural ISP framework provides high rendering accuracy, scalability, and flexibility for diverse photo-editing operations with competitive results.
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
Community
Modular Neural Image Signal Processing
We present a modular neural image signal processing (ISP) framework that produces high-quality display-referred images while providing a high degree of modularity with explicit control over multiple intermediate stages of the rendering pipeline. Our ISP is fully differentiable and requires no manual tuning, and its modular structure not only improves rendering accuracy but also enhances scalability, debuggability, generalization to unseen cameras, and flexibility to support different user-preference picture styles within a lightweight and efficient design.
On top of this modular neural ISP, we developed a user-interactive photo-editing tool that supports diverse editing operations, different picture styles, and enables unlimited post-editable re-rendering and re-styling. The tool accepts DNG raw images from any camera as well as sRGB images from third-party sources. Across multiple test sets, our method consistently delivers competitive qualitative and quantitative performance.
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- Edit-aware RAW Reconstruction (2025)
- Video4Edit: Viewing Image Editing as a Degenerate Temporal Process (2025)
- From Cheap to Pro: A Learning-based Adaptive Camera Parameter Network for Professional-Style Imaging (2025)
- IBGS: Image-Based Gaussian Splatting (2025)
- Scaling Instruction-Based Video Editing with a High-Quality Synthetic Dataset (2025)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment:
@librarian-bot
recommend
Models citing this paper 0
No model linking this paper
Datasets citing this paper 0
No dataset linking this paper
Spaces citing this paper 0
No Space linking this paper
Collections including this paper 0
No Collection including this paper