Physics-Guided Rectified Flow for Low-light RAW Image Enhancement

• URL: http://arxiv.org/abs/2509.08330v1
• Date: Wed, 10 Sep 2025 07:08:43 GMT
• Title: Physics-Guided Rectified Flow for Low-light RAW Image Enhancement
• Authors: Juntai Zeng,
• Abstract summary: Enhancing RAW images captured under low light conditions is a challenging task.<n>Recent deep learning based RAW enhancement methods have shifted from using real paired data to relying on synthetic datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Enhancing RAW images captured under low light conditions is a challenging task. Recent deep learning based RAW enhancement methods have shifted from using real paired data to relying on synthetic datasets. These synthetic datasets are typically generated by physically modeling sensor noise, but existing approaches often consider only additive noise, ignore multiplicative components, and rely on global calibration that overlooks pixel level manufacturing variations. As a result, such methods struggle to accurately reproduce real sensor noise. To address these limitations, this paper derives a noise model from the physical noise generation mechanisms that occur under low illumination and proposes a novel composite model that integrates both additive and multiplicative noise. To solve the model, we introduce a physics based per pixel noise simulation and calibration scheme that estimates and synthesizes noise for each individual pixel, thereby overcoming the restrictions of traditional global calibration and capturing spatial noise variations induced by microscopic CMOS manufacturing differences. Motivated by the strong performance of rectified flow methods in image generation and processing, we further combine the physics-based noise synthesis with a rectified flow generative framework and present PGRF a physics-guided rectified flow framework for low light image enhancement. PGRF leverages the ability of rectified flows to model complex data distributions and uses physical guidance to steer the generation toward the desired clean image. To validate the effectiveness of the proposed model, we established the LLID dataset, an indoor low light benchmark captured with the Sony A7S II camera. Experimental results demonstrate that the proposed framework achieves significant improvements in low light RAW image enhancement.

Related papers

• Denoising the Deep Sky: Physics-Based CCD Noise Formation for Astronomical Imaging [47.83642412662346]
  Learning-based denoising is promising, yet progress is hindered by scarce paired training data.<n>We propose a physics-based noise synthesis framework tailored to CCD noise formation.
  arXiv  Detail & Related papers  (2026-01-30T18:47:54Z)
• Hybrid Event Frame Sensors: Modeling, Calibration, and Simulation [46.93612436763656]
  Event frame hybrid sensors integrate an Active Pixel Sensor (APS) and an Event Vision Sensor (EVS) within a single chip.<n>We present the first unified, statistics-based imaging noise model that jointly describes the noise behavior of APS and EVS pixels.
  arXiv  Detail & Related papers  (2025-11-22T12:32:07Z)
• Enhanced Confocal Laser Scanning Microscopy with Adaptive Physics Informed Deep Autoencoders [0.0]
  We present a physics-informed deep learning framework to address limitations in Confocal Laser Scanning Microscopy.<n>The model reconstructs high fidelity images from heavily noisy inputs by using convolutional and transposed convolutional layers.
  arXiv  Detail & Related papers  (2025-01-24T18:32:34Z)
• Novel Hybrid Integrated Pix2Pix and WGAN Model with Gradient Penalty for Binary Images Denoising [1.8434042562191815]
  This paper introduces a novel approach to image denoising that leverages the advantages of Generative Adversarial Networks (GANs) We propose a model that combines elements of the Pix2Pix model and the Wasserstein GAN (WGAN) with Gradient Penalty (WGAN-GP)
  arXiv  Detail & Related papers  (2024-07-16T15:50:45Z)
• NM-FlowGAN: Modeling sRGB Noise without Paired Images using a Hybrid Approach of Normalizing Flows and GAN [9.81778202920426]
  NM-FlowGAN is a hybrid approach that exploits the strengths of both GAN and Normalizing Flows. Our method synthesizes noise using clean images and factors that affect noise characteristics, such as easily obtainable parameters like camera type and ISO settings. In our experiments, our NM-FlowGAN outperforms other baselines in the sRGB noise synthesis task.
  arXiv  Detail & Related papers  (2023-12-15T09:09:25Z)
• Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model [83.9497193551511]
  We introduce Lighting Every Darkness (LED), which is effective regardless of the digital gain or the camera sensor. LED eliminates the need for explicit noise model calibration, instead utilizing an implicit fine-tuning process that allows quick deployment and requires minimal data. LED also allows researchers to focus more on deep learning advancements while still utilizing sensor engineering benefits.
  arXiv  Detail & Related papers  (2023-08-07T10:09:11Z)
• Towards General Low-Light Raw Noise Synthesis and Modeling [37.87312467017369]
  We introduce a new perspective to synthesize the signal-independent noise by a generative model. Specifically, we synthesize the signal-dependent and signal-independent noise in a physics- and learning-based manner. In this way, our method can be considered as a general model, that is, it can simultaneously learn different noise characteristics for different ISO levels.
  arXiv  Detail & Related papers  (2023-07-31T09:10:10Z)
• Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-Regulation [55.07472635587852]
  Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast. These approaches encounter persistent challenges in efficiently mitigating dynamic noise and accommodating diverse low-light scenarios. We first propose a method for estimating the noise level in low light images in a quick and accurate way. We then devise a Learnable Illumination Interpolator (LII) to satisfy general constraints between illumination and input.
  arXiv  Detail & Related papers  (2023-05-17T13:56:48Z)
• SAR Despeckling using a Denoising Diffusion Probabilistic Model [52.25981472415249]
  The presence of speckle degrades the image quality and adversely affects the performance of SAR image understanding applications. We introduce SAR-DDPM, a denoising diffusion probabilistic model for SAR despeckling. The proposed method achieves significant improvements in both quantitative and qualitative results over the state-of-the-art despeckling methods.
  arXiv  Detail & Related papers  (2022-06-09T14:00:26Z)
• Rethinking Noise Synthesis and Modeling in Raw Denoising [75.55136662685341]
  We introduce a new perspective to synthesize noise by directly sampling from the sensor's real noise. It inherently generates accurate raw image noise for different camera sensors.
  arXiv  Detail & Related papers  (2021-10-10T10:45:24Z)
• CERL: A Unified Optimization Framework for Light Enhancement with Realistic Noise [81.47026986488638]
  Low-light images captured in the real world are inevitably corrupted by sensor noise. Existing light enhancement methods either overlook the important impact of real-world noise during enhancement, or treat noise removal as a separate pre- or post-processing step. We present Coordinated Enhancement for Real-world Low-light Noisy Images (CERL), that seamlessly integrates light enhancement and noise suppression parts into a unified and physics-grounded framework.
  arXiv  Detail & Related papers  (2021-08-01T15:31:15Z)
• Designing a Practical Degradation Model for Deep Blind Image Super-Resolution [134.9023380383406]
  Single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images. This paper proposes to design a more complex but practical degradation model that consists of randomly shuffled blur, downsampling and noise degradations.
  arXiv  Detail & Related papers  (2021-03-25T17:40:53Z)
• A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising [34.98772175073111]
  We present a highly accurate noise formation model based on the characteristics of CMOS photosensors. We also propose a method to calibrate the noise parameters for available modern digital cameras.
  arXiv  Detail & Related papers  (2020-03-28T09:16:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.