# Binarized Low-light Raw Video Enhancement

arXiv: https://arxiv.org/abs/2403.19944 · HTML: https://mlsanity.com/paper/2403.19944/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 55 references that are in the map.
- First posted on arXiv 29 Mar 2024.

## Builds on (most cited first, 30 of 32)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://mlsanity.com/paper/1603.05279/) (2016)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://mlsanity.com/paper/1511.00363/) (2015)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [Learning to See in the Dark](https://mlsanity.com/paper/1805.01934/) (2018)
- [Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm](https://mlsanity.com/paper/1808.00278/) (2018)
- [Binary Neural Networks: A Survey](https://mlsanity.com/paper/2004.03333/) (2020)
- [ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions](https://mlsanity.com/paper/2003.03488/) (2020)
- [Forward and Backward Information Retention for Accurate Binary Neural Networks](https://mlsanity.com/paper/1909.10788/) (2019)
- [A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising](https://mlsanity.com/paper/2003.12751/) (2020)
- [FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation](https://mlsanity.com/paper/1907.01361/) (2019)
- [Supervised Raw Video Denoising with a Benchmark Dataset on Dynamic Scenes](https://mlsanity.com/paper/2003.14013/) (2020)
- [A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift](https://mlsanity.com/paper/2206.10810/) (2022)
- [Physics-based Noise Modeling for Extreme Low-light Photography](https://mlsanity.com/paper/2108.02158/) (2021)
- [DVDnet: A Fast Network for Deep Video Denoising](https://mlsanity.com/paper/1906.11890/) (2019)
- [Instance Segmentation in the Dark](https://mlsanity.com/paper/2304.14298/) (2023)
- [Learning Frequency Domain Approximation for Binary Neural Networks](https://mlsanity.com/paper/2103.00841/) (2021)
- [Efficient Multi-Stage Video Denoising with Recurrent Spatio-Temporal Fusion](https://mlsanity.com/paper/2103.05407/) (2021)
- [What Makes a "Good" Data Augmentation in Knowledge Distillation -- A Statistical Perspective](https://mlsanity.com/paper/2012.02909/) (2020)
- [RawHDR: High Dynamic Range Image Reconstruction from a Single Raw Image](https://mlsanity.com/paper/2309.02020/) (2023)
- [Low Light Video Enhancement using Synthetic Data Produced with an Intermediate Domain Mapping](https://mlsanity.com/paper/2007.09187/) (2020)
- [Towards Low Light Enhancement with RAW Images](https://mlsanity.com/paper/2112.14022/) (2021)
- [Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Ones](https://mlsanity.com/paper/2204.05532/) (2022)
- [Binarized Spectral Compressive Imaging](https://mlsanity.com/paper/2305.10299/) (2023)
- [Basic Binary Convolution Unit for Binarized Image Restoration Network](https://mlsanity.com/paper/2210.00405/) (2022)
- [Accurate and Compact Convolutional Neural Networks with Trained Binarization](https://mlsanity.com/paper/1909.11366/) (2019)
- [Estimator Meets Equilibrium Perspective: A Rectified Straight Through Estimator for Binary Neural Networks Training](https://mlsanity.com/paper/2308.06689/) (2023)
- [Estimating Fine-Grained Noise Model via Contrastive Learning](https://mlsanity.com/paper/2204.01716/) (2022)

## Built on by (most cited first, 17 of 17)

- [Binarized Mamba-Transformer for Lightweight Quad Bayer HybridEVS Demosaicing](https://mlsanity.com/paper/2503.16134/) (2025)
- [Low-Light Video Enhancement via Spatial-Temporal Consistent Decomposition](https://mlsanity.com/paper/2405.15660/) (2024)
- [UltraFast-LiNET: Light-weight multi-scale shift convolutional network for real-time low-light image enhancement](https://mlsanity.com/paper/2512.02965/) (2025)
- [Low-Light Object Tracking: A Benchmark](https://mlsanity.com/paper/2408.11463/) (2024)
- [SAM-Based Building Change Detection with Distribution-Aware Fourier Adaptation and Edge-Constrained Warping](https://mlsanity.com/paper/2504.12619/) (2025)
- [Efficient RAW Image Deblurring with Adaptive Frequency Modulation](https://mlsanity.com/paper/2505.24407/) (2025)
- [Optimizing 4D Lookup Table for Low-light Video Enhancement via Wavelet Priori](https://mlsanity.com/paper/2409.08585/) (2024)
- [BDC-Occ: Binarized Deep Convolution Unit For Binarized Occupancy Network](https://mlsanity.com/paper/2405.17037/) (2024)
- [Spatial-Spectral Binarized Neural Network for Panchromatic and Multi-spectral Images Fusion](https://mlsanity.com/paper/2509.23321/) (2025)
- [Dynamic Weight-based Temporal Aggregation for Low-light Video Enhancement Under Extreme Noise](https://mlsanity.com/paper/2510.09450/) (2025)
- [BinaryDemoire: Moir'e-Aware Binarization for Image Demoir'eing](https://mlsanity.com/paper/2602.03176/) (2026)
- [Low-Light Video Enhancement with An Effective Spatial-Temporal Decomposition Paradigm](https://mlsanity.com/paper/2602.08699/) (2026)
- [NEC-Diff: Noise-Robust Event-RAW Complementary Diffusion for Seeing Motion in Extreme Darkness](https://mlsanity.com/paper/2603.20005/) (2026)
- [UHD-GPGNet: UHD Video Denoising via Gaussian-Process-Guided Local Spatio-Temporal Modeling](https://mlsanity.com/paper/2604.11014/) (2026)
- [AnyMod-LLVE: Low-Light Video Enhancement with Modality-Agnostic Inference](https://mlsanity.com/paper/2606.11186/) (2026)
- [EeveeDark: A Binary Neural Framework for Low-Light Video Enhancement via Event-Guided Sensor-Level Fusion](https://mlsanity.com/paper/2607.06217/) (2026)
- [Binarized High-Efficiency RAW Video Restoration and Beyond](https://mlsanity.com/paper/2608.16756/) (2026)

## Abstract (arXiv)

Recently, deep neural networks have achieved excellent performance on low-light raw video enhancement. However, they often come with high computational complexity and large memory costs, which hinder their applications on resource-limited devices. In this paper, we explore the feasibility of applying the extremely compact binary neural network (BNN) to low-light raw video enhancement. Nevertheless, there are two main issues with binarizing video enhancement models. One is how to fuse the temporal information to improve low-light denoising without complex modules. The other is how to narrow the performance gap between binary convolutions with the full precision ones. To address the first issue, we introduce a spatial-temporal shift operation, which is easy-to-binarize and effective. The temporal shift efficiently aggregates the features of neighbor frames and the spatial shift handles the misalignment caused by the large motion in videos. For the second issue, we present a distribution-aware binary convolution, which captures the distribution characteristics of real-valued input and incorporates them into plain binary convolutions to alleviate the degradation in performance. Extensive quantitative and qualitative experiments have shown our high-efficiency binarized low-light raw video enhancement method can attain a promising performance.