# Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets Noise Modeling

arXiv: https://arxiv.org/abs/2207.06103 · HTML: https://mlsanity.com/paper/2207.06103/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Learning to See in the Dark](https://mlsanity.com/paper/1805.01934/) (2018)
- [Unprocessing Images for Learned Raw Denoising](https://mlsanity.com/paper/1811.11127/) (2018)
- [Benchmarking Denoising Algorithms with Real Photographs](https://mlsanity.com/paper/1707.01313/) (2017)
- [Low-Light Image and Video Enhancement Using Deep Learning: A Survey](https://mlsanity.com/paper/2104.10729/) (2021)
- [A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising](https://mlsanity.com/paper/2003.12751/) (2020)
- [Supervised Raw Video Denoising with a Benchmark Dataset on Dynamic Scenes](https://mlsanity.com/paper/2003.14013/) (2020)
- [Practical Deep Raw Image Denoising on Mobile Devices](https://mlsanity.com/paper/2010.06935/) (2020)
- [Physics-based Noise Modeling for Extreme Low-light Photography](https://mlsanity.com/paper/2108.02158/) (2021)
- [Rethinking Noise Synthesis and Modeling in Raw Denoising](https://mlsanity.com/paper/2110.04756/) (2021)
- [Handheld Mobile Photography in Very Low Light](https://mlsanity.com/paper/1910.11336/) (2019)
- [Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation](https://mlsanity.com/paper/1904.12945/) (2019)
- [High-level numerical simulations of noise in CCD and CMOS photosensors: review and tutorial](https://mlsanity.com/paper/1412.4031/) (2014)

## Built on by (most cited first, 23 of 23)

- [Wave-Mamba: Wavelet State Space Model for Ultra-High-Definition Low-Light Image Enhancement](https://mlsanity.com/paper/2408.01276/) (2024)
- [ExposureDiffusion: Learning to Expose for Low-light Image Enhancement](https://mlsanity.com/paper/2307.07710/) (2023)
- [Towards General Low-Light Raw Noise Synthesis and Modeling](https://mlsanity.com/paper/2307.16508/) (2023)
- [Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond](https://mlsanity.com/paper/2212.10772/) (2022)
- [From Chaos to Clarity: 3DGS in the Dark](https://mlsanity.com/paper/2406.08300/) (2024)
- [MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results](https://mlsanity.com/paper/2406.07006/) (2024)
- [Learning Physics-Informed Noise Models from Dark Frames for Low-Light Raw Image Denoising](https://mlsanity.com/paper/2310.09126/) (2023)
- [RViDeformer: Efficient Raw Video Denoising Transformer with a Larger Benchmark Dataset](https://mlsanity.com/paper/2305.00767/) (2023)
- [Visibility Constrained Wide-band Illumination Spectrum Design for Seeing-in-the-Dark](https://mlsanity.com/paper/2303.11642/) (2023)
- [Complementary Advantages: Exploiting Cross-Field Frequency Correlation for NIR-Assisted Image Denoising](https://mlsanity.com/paper/2412.16645/) (2024)
- [Dark-ISP: Enhancing RAW Image Processing for Low-Light Object Detection](https://mlsanity.com/paper/2509.09183/) (2025)
- [DiffuseRAW: End-to-End Generative RAW Image Processing for Low-Light Images](https://mlsanity.com/paper/2402.18575/) (2023)
- [Self-Supervised Monocular Depth Estimation in the Dark: Towards Data Distribution Compensation](https://mlsanity.com/paper/2404.13854/) (2024)
- [Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising](https://mlsanity.com/paper/2503.11262/) (2025)
- [An End-to-End Real-World Camera Imaging Pipeline](https://mlsanity.com/paper/2411.10773/) (2024)
- [Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model](https://mlsanity.com/paper/2308.03448/) (2023)
- [TS-Diff: Two-Stage Diffusion Model for Low-Light RAW Image Enhancement](https://mlsanity.com/paper/2505.04281/) (2025)
- [DEPFusion: Dual-Domain Enhancement and Priority-Guided Mamba Fusion for UAV Multispectral Object Detection](https://mlsanity.com/paper/2509.07327/) (2025)
- [Why Low-Light Cameras Go Color Blind: Removing Color Bias in Raw Denoising](https://mlsanity.com/paper/2607.11090/) (2026)
- [Seeing Through the Noisy Dark: Towards Real-world Low-Light Image Enhancement and Denoising](https://mlsanity.com/paper/2210.00545/) (2022)
- [A Real-time Endoscopic Image Denoising System](https://mlsanity.com/paper/2506.15395/) (2025)
- [DepthDark: Robust Monocular Depth Estimation for Low-Light Environments](https://mlsanity.com/paper/2507.18243/) (2025)
- [Physics-Guided Rectified Flow for Low-light RAW Image Enhancement](https://mlsanity.com/paper/2509.08330/) (2025)

## Abstract (arXiv)

Low-light raw denoising is an important and valuable task in computational photography where learning-based methods trained with paired real data are mainstream. However, the limited data volume and complicated noise distribution have constituted a learnability bottleneck for paired real data, which limits the denoising performance of learning-based methods. To address this issue, we present a learnability enhancement strategy to reform paired real data according to noise modeling. Our strategy consists of two efficient techniques: shot noise augmentation (SNA) and dark shading correction (DSC). Through noise model decoupling, SNA improves the precision of data mapping by increasing the data volume and DSC reduces the complexity of data mapping by reducing the noise complexity. Extensive results on the public datasets and real imaging scenarios collectively demonstrate the state-of-the-art performance of our method. Our code is available at: https://github.com/megvii-research/PMN.