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PGRF: Physics-Guided Rectified Flow for Low-Light RAW Image Enhancement.

Juntai Zeng¹, Qingyun Yang¹

¹Academy for Advanced Interdisciplinary Studies, Northeast Normal University, Changchun 130024, China.

Journal of Imaging

|November 26, 2025

Summary

This study introduces a new physics-guided method for enhancing low-light RAW images. It uses a novel composite noise model and per-pixel calibration for more accurate sensor noise simulation, improving image quality.

Keywords:

low-light denoising noise modeling rectified flow

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Area of Science:

Computational Photography
Image Signal Processing
Deep Learning

Background:

Low-light RAW image enhancement is challenging.
Existing deep learning methods struggle with realistic sensor noise simulation, often neglecting multiplicative noise and pixel variations.
Global calibration methods fail to capture spatial noise differences.

Purpose of the Study:

To develop a physically grounded composite noise model for accurate sensor noise simulation.
To propose a per-pixel noise simulation and calibration strategy for capturing spatial noise variations.
To introduce a physics-guided rectified-flow framework (PGRF) for low-light RAW image enhancement.

Main Methods:

Developed a composite noise model incorporating additive and multiplicative noise.
Implemented a per-pixel noise simulation and calibration strategy.
Integrated physics-based noise synthesis into a rectified-flow generative framework (PGRF).
Created the LLID benchmark dataset for low-light RAW image enhancement.

Main Results:

The proposed PGRF framework achieves substantial improvements over state-of-the-art methods.
Physics-based calibration captures spatial noise variations due to CMOS sensor fabrication differences.
The method faithfully reproduces complex statistics of real sensor noise.

Conclusions:

PGRF offers a significant advancement in low-light RAW image enhancement by addressing limitations in noise modeling and calibration.
The per-pixel calibration strategy effectively handles spatial noise variations.
The physics-guided approach leads to superior image quality in challenging low-light conditions.