# Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion Deblurring

arXiv: https://arxiv.org/abs/2401.00027 · HTML: https://mlsanity.com/paper/2401.00027/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 44 references that are in the map.
- First posted on arXiv 29 Dec 2023.

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

- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [Simple Baselines for Image Restoration](https://mlsanity.com/paper/2204.04676/) (2022)
- [FDA: Fourier Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2004.05498/) (2020)
- [DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks](https://mlsanity.com/paper/1711.07064/) (2017)
- [DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better](https://mlsanity.com/paper/1908.03826/) (2019)
- [Scale-recurrent Network for Deep Image Deblurring](https://mlsanity.com/paper/1802.01770/) (2018)
- [Rethinking Coarse-to-Fine Approach in Single Image Deblurring](https://mlsanity.com/paper/2108.05054/) (2021)
- [MAXIM: Multi-Axis MLP for Image Processing](https://mlsanity.com/paper/2201.02973/) (2022)
- [BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation](https://mlsanity.com/paper/2004.02147/) (2020)
- [NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection](https://mlsanity.com/paper/1904.07392/) (2019)
- [Deep Stacked Hierarchical Multi-patch Network for Image Deblurring](https://mlsanity.com/paper/1904.03468/) (2019)
- [Deblurring by Realistic Blurring](https://mlsanity.com/paper/2004.01860/) (2020)
- [FcaNet: Frequency Channel Attention Networks](https://mlsanity.com/paper/2012.11879/) (2020)
- [Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring](https://mlsanity.com/paper/2211.12250/) (2022)
- [Efficient and Explicit Modelling of Image Hierarchies for Image Restoration](https://mlsanity.com/paper/2303.00748/) (2023)
- [Stripformer: Strip Transformer for Fast Image Deblurring](https://mlsanity.com/paper/2204.04627/) (2022)
- [Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training](https://mlsanity.com/paper/1911.07410/) (2019)
- [Multi-level Wavelet Convolutional Neural Networks](https://mlsanity.com/paper/1907.03128/) (2019)
- [Realistic Blur Synthesis for Learning Image Deblurring](https://mlsanity.com/paper/2202.08771/) (2022)
- [BANet: Blur-aware Attention Networks for Dynamic Scene Deblurring](https://mlsanity.com/paper/2101.07518/) (2021)
- [MSTR: Multi-Scale Transformer for End-to-End Human-Object Interaction Detection](https://mlsanity.com/paper/2203.14709/) (2022)
- [Adaptive wavelet distillation from neural networks through interpretations](https://mlsanity.com/paper/2107.09145/) (2021)

## Built on by (most cited first, 24 of 24)

- [Efficient Visual State Space Model for Image Deblurring](https://mlsanity.com/paper/2405.14343/) (2024)
- [MP-HSIR: A Multi-Prompt Framework for Universal Hyperspectral Image Restoration](https://mlsanity.com/paper/2503.09131/) (2025)
- [From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting](https://mlsanity.com/paper/2506.23042/) (2025)
- [Mamba-Diffusion Model with Learnable Wavelet for Controllable Symbolic Music Generation](https://mlsanity.com/paper/2505.03314/) (2025)
- [Cross-Frequency Implicit Neural Representation with Self-Evolving Parameters](https://mlsanity.com/paper/2504.10929/) (2025)
- [WaveFormer: A Lightweight Transformer Model for sEMG-based Gesture Recognition](https://mlsanity.com/paper/2506.11168/) (2025)
- [Global Modeling Matters: A Fast, Lightweight and Effective Baseline for Efficient Image Restoration](https://mlsanity.com/paper/2507.13663/) (2025)
- [Towards Unified Image Deblurring using a Mixture-of-Experts Decoder](https://mlsanity.com/paper/2508.06228/) (2025)
- [Efficient Concertormer for Image Deblurring and Beyond](https://mlsanity.com/paper/2404.06135/) (2024)
- [DLEN: Dual Branch of Transformer for Low-Light Image Enhancement in Dual Domains](https://mlsanity.com/paper/2501.12235/) (2025)
- [FCVSR: A Frequency-aware Method for Compressed Video Super-Resolution](https://mlsanity.com/paper/2502.06431/) (2025)
- [EEMS: Edge-Prompt Enhanced Medical Image Segmentation Based on Learnable Gating Mechanism](https://mlsanity.com/paper/2510.11287/) (2025)
- [Near-infrared Image Deblurring and Event Denoising with Synergistic Neuromorphic Imaging](https://mlsanity.com/paper/2503.01193/) (2025)
- [Degradation-Aware Metric Prompting for Hyperspectral Image Restoration](https://mlsanity.com/paper/2512.20251/) (2025)
- [Learnable Multi-level Discrete Wavelet Transforms for 3D Gaussian Splatting Frequency Modulation](https://mlsanity.com/paper/2602.14199/) (2026)
- [UnfoldLDM: Degradation-Aware Unfolding with Iterative Latent Diffusion Priors for Blind Image Restoration](https://mlsanity.com/paper/2511.18152/) (2025)
- [A low-complexity method for efficient depth-guided image deblurring](https://mlsanity.com/paper/2601.03924/) (2026)
- [Toward Generalizable Deblurring: Leveraging Massive Blur Priors with Linear Attention for Real-World Scenarios](https://mlsanity.com/paper/2601.06525/) (2026)
- [Fast Low-light Enhancement and Deblurring for 3D Dark Scenes](https://mlsanity.com/paper/2603.08133/) (2026)
- [Leveraging Multimodal Large Language Models for All-in-One Image Restoration via a Mixture of Frequency Experts](https://mlsanity.com/paper/2605.11444/) (2026)
- [FAF-CD: Frequency-Aware Fusion for Change Detection under Imperfect Multimodal Remote Sensing](https://mlsanity.com/paper/2606.03114/) (2026)
- [CogSENet: Blind Image Deblurring with Blur-Conditioned Semantic Routing and Explicit Frequency Fusion](https://mlsanity.com/paper/2606.30030/) (2026)
- [RCL-Mamba: A Dual-domain State Space Model for Measurement-oriented Image Restoration in Rotational Sparse-View Scanning Computed Laminography](https://mlsanity.com/paper/2606.31353/) (2026)
- [EvBS: Event-guided Blur Synthesis for Domain-adaptive Motion Deblurring](https://mlsanity.com/paper/2608.08066/) (2026)

## Abstract (arXiv)

Coarse-to-fine schemes are widely used in traditional single-image motion deblur; however, in the context of deep learning, existing multi-scale algorithms not only require the use of complex modules for feature fusion of low-scale RGB images and deep semantics, but also manually generate low-resolution pairs of images that do not have sufficient confidence. In this work, we propose a multi-scale network based on single-input and multiple-outputs(SIMO) for motion deblurring. This simplifies the complexity of algorithms based on a coarse-to-fine scheme. To alleviate restoration defects impacting detail information brought about by using a multi-scale architecture, we combine the characteristics of real-world blurring trajectories with a learnable wavelet transform module to focus on the directional continuity and frequency features of the step-by-step transitions between blurred images to sharp images. In conclusion, we propose a multi-scale network with a learnable discrete wavelet transform (MLWNet), which exhibits state-of-the-art performance on multiple real-world deblurred datasets, in terms of both subjective and objective quality as well as computational efficiency.