When Extreme Darkness Meets Motion Blur: MeanFlow for Unified RAW Restoration

• URL: http://arxiv.org/abs/2608.01720v1
• Date: Mon, 03 Aug 2026 05:38:47 GMT
• Title: When Extreme Darkness Meets Motion Blur: MeanFlow for Unified RAW Restoration
• Abstract summary: We present a framework for robust extremely low-light RAW enhancement under realistic acquisition degradations.<n>To our knowledge, this is the first work to formulate extremely low-light RAW enhancement under realistic motion-degraded acquisition.
• Score: 2.3881445172169085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extremely low-light RAW enhancement aims to recover severely attenuated sensor signals, yet existing methods often focus on illumination and noise while overlooking the motion-induced degradations inherent in practical low-light imaging. We present a framework for robust extremely low-light RAW enhancement under realistic acquisition degradations. First, we introduce See in the Degraded Extremely Dark (SIDED), a new dataset that applies controlled motion degradation to extremely low-light RAW pairs while retaining their original sensor noise. Second, we propose a unified RAW tokenizer equipped with explicit domain-conditioned representation calibration to align extremely low-light and well-exposed RAW data, followed by a MeanFlow that performs enhancement in a single function evaluation. To our knowledge, this is the first work to formulate extremely low-light RAW enhancement under realistic motion-degraded acquisition and address it with MeanFlow. We further introduce a physics-guided refinement model to strengthen illumination--reflectance consistency, pixel fidelity, and color preservation without incurring additional inference cost. Extensive experiments demonstrate that our framework achieves state-of-the-art performance in extremely low-light RAW enhancement, and robustly handles coupled motion and noise degradations.

Related papers

• ExpoCM: Exposure-Aware One-Step Generative Single-Image HDR Reconstruction [60.873186669266836]
  Single-image HDR reconstruction aims to recover high dynamic range radiance from a single low dynamic range (LDR) input, but remains highly ill-posed due to detail saturation in over-exposed regions and noise amplification in under-exposed areas.<n>We propose ExpoCM, a novel one-step generative HDR reconstruction framework that reformulates HDR reconstruction as a Probability Flow ODE (PF-ODE) and constructs exposure-aware consistency trajectories via exposure-dependent perturbations.<n>Experiments on the HDR-REAL, HDR-EYE, and AIM2025 benchmarks demonstrate that ExpoCM achieves state-of-the-art fidelity
  arXiv  Detail & Related papers  (2026-05-04T11:06:49Z)
• NEC-Diff: Noise-Robust Event-RAW Complementary Diffusion for Seeing Motion in Extreme Darkness [32.700771429075694]
  NEC-Diff is a novel diffusion-based event-RAW hybrid imaging framework.<n>It extracts reliable information from heavily noisy signals to reconstruct fine scene structures.<n>We construct the REAL dataset which provides 47,800 pixel-aligned low-light RAW images, events, and high-quality references under 0.001-0.8 lux illumination.
  arXiv  Detail & Related papers  (2026-03-20T14:51:24Z)
• Learning to See in the Extremely Dark [41.386150786725295]
  We propose a paired-to-paired data synthesis pipeline capable of generating well-calibrated extremely low-light RAW images.<n>A large-scale paired dataset named See-in-the-Extremely-Dark (SIED) is used to benchmark low-light RAW image enhancement approaches.<n>A diffusion-based framework is proposed to restore visually pleasing results from extremely low-SNR RAW inputs.
  arXiv  Detail & Related papers  (2025-06-26T10:24:07Z)
• LUMINA-Net: Low-light Upgrade through Multi-stage Illumination and Noise Adaptation Network for Image Enhancement [26.585985828583304]
  Low-light image enhancement (LLIE) is a crucial task in computer vision aimed at enhancing the visual fidelity of images captured under low-illumination conditions.<n>We propose LUMINA-Net, an unsupervised deep learning framework that learns adaptive priors from low-light image pairs by integrating multi-stage illumination and reflectance modules.
  arXiv  Detail & Related papers  (2025-02-21T03:37:58Z)
• Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement [71.13353154514418]
  Low-light image enhancement, particularly in cross-domain tasks such as mapping from the raw domain to the sRGB domain, remains a significant challenge.<n>We propose a novel Mamba-based method customized for low light RAW images, called RAWMamba, to effectively handle raw images with different CFAs.<n>By bridging demosaicing and denoising, better enhancement for low light RAW images is achieved.
  arXiv  Detail & Related papers  (2024-09-11T06:12:03Z)
• LDM-ISP: Enhancing Neural ISP for Low Light with Latent Diffusion Models [54.93010869546011]
  We propose to leverage the pre-trained latent diffusion model to perform the neural ISP for enhancing extremely low-light images.<n>Specifically, to tailor the pre-trained latent diffusion model to operate on the RAW domain, we train a set of lightweight taming modules.<n>We observe different roles of UNet denoising and decoder reconstruction in the latent diffusion model, which inspires us to decompose the low-light image enhancement task into latent-space low-frequency content generation and decoding-phase high-frequency detail maintenance.
  arXiv  Detail & Related papers  (2023-12-02T04:31:51Z)
• Towards Low Light Enhancement with RAW Images [101.35754364753409]
  We make the first benchmark effort to elaborate on the superiority of using RAW images in the low light enhancement. We develop a new evaluation framework, Factorized Enhancement Model (FEM), which decomposes the properties of RAW images into measurable factors. A RAW-guiding Exposure Enhancement Network (REENet) is developed, which makes trade-offs between the advantages and inaccessibility of RAW images in real applications.
  arXiv  Detail & Related papers  (2021-12-28T07:27:51Z)
• 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)
• Progressive Joint Low-light Enhancement and Noise Removal for Raw Images [10.778200442212334]
  Low-light imaging on mobile devices is typically challenging due to insufficient incident light coming through the relatively small aperture. We propose a low-light image processing framework that performs joint illumination adjustment, color enhancement, and denoising. Our framework does not need to recollect massive data when being adapted to another camera model.
  arXiv  Detail & Related papers  (2021-06-28T16:43:52Z)
• Unsupervised Low-light Image Enhancement with Decoupled Networks [103.74355338972123]
  We learn a two-stage GAN-based framework to enhance the real-world low-light images in a fully unsupervised fashion. Our proposed method outperforms the state-of-the-art unsupervised image enhancement methods in terms of both illumination enhancement and noise reduction.
  arXiv  Detail & Related papers  (2020-05-06T13:37:08Z)

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.