# SDWNet: A Straight Dilated Network with Wavelet Transformation for Image Deblurring

arXiv: https://arxiv.org/abs/2110.05803 · HTML: https://mlsanity.com/paper/2110.05803/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 41 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 18 of 18)

- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [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)
- [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)
- [Multi-level Wavelet-CNN for Image Restoration](https://mlsanity.com/paper/1805.07071/) (2018)
- [Human-Aware Motion Deblurring](https://mlsanity.com/paper/2001.06816/) (2020)
- [Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal](https://mlsanity.com/paper/1503.00593/) (2015)
- [Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring](https://mlsanity.com/paper/2004.05343/) (2020)
- [Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training](https://mlsanity.com/paper/1911.07410/) (2019)
- [From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur](https://mlsanity.com/paper/1612.02583/) (2016)
- [Inception Convolution with Efficient Dilation Search](https://mlsanity.com/paper/2012.13587/) (2020)
- [Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections](https://mlsanity.com/paper/1603.09056/) (2016)

## Built on by (most cited first, 26 of 26)

- [Intriguing Findings of Frequency Selection for Image Deblurring](https://mlsanity.com/paper/2111.11745/) (2021)
- [Wave-Mamba: Wavelet State Space Model for Ultra-High-Definition Low-Light Image Enhancement](https://mlsanity.com/paper/2408.01276/) (2024)
- [Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion Deblurring](https://mlsanity.com/paper/2401.00027/) (2023)
- [Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2203.07656/) (2022)
- [Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-Resolution](https://mlsanity.com/paper/2304.01994/) (2023)
- [Multi-scale frequency separation network for image deblurring](https://mlsanity.com/paper/2206.00798/) (2022)
- [WaveMamba: Wavelet-Driven Mamba Fusion for RGB-Infrared Object Detection](https://mlsanity.com/paper/2507.18173/) (2025)
- [Prior-guided Hierarchical Harmonization Network for Efficient Image Dehazing](https://mlsanity.com/paper/2503.01136/) (2025)
- [FPANet: Frequency-based Video Demoireing using Frame-level Post Alignment](https://mlsanity.com/paper/2301.07330/) (2023)
- [Deep learning in motion deblurring: current status, benchmarks and future prospects](https://mlsanity.com/paper/2401.05055/) (2024)
- [Complementary Advantages: Exploiting Cross-Field Frequency Correlation for NIR-Assisted Image Denoising](https://mlsanity.com/paper/2412.16645/) (2024)
- [WaveletFormerNet: A Transformer-based Wavelet Network for Real-world Non-homogeneous and Dense Fog Removal](https://mlsanity.com/paper/2401.04550/) (2024)
- [All-in-One Image Restoration via Causal-Deconfounding Wavelet-Disentangled Prompt Network](https://mlsanity.com/paper/2603.03839/) (2026)
- [Haze-Aware Attention Network for Single-Image Dehazing](https://mlsanity.com/paper/2407.11505/) (2024)
- [Restoring Real-World Degraded Events Improves Deblurring Quality](https://mlsanity.com/paper/2407.20502/) (2024)
- [Towards Context-aware Convolutional Network for Image Restoration](https://mlsanity.com/paper/2412.11008/) (2024)
- [Dynamic Scene Deblurring Based on Continuous Cross-Layer Attention Transmission](https://mlsanity.com/paper/2206.11476/) (2022)
- [DedustNet: A Frequency-dominated Swin Transformer-based Wavelet Network for Agricultural Dust Removal](https://mlsanity.com/paper/2401.04750/) (2024)
- [Look-Around Before You Leap: High-Frequency Injected Transformer for Image Restoration](https://mlsanity.com/paper/2404.00279/) (2024)
- [AMSA-UNet: An Asymmetric Multiple Scales U-net Based on Self-attention for Deblurring](https://mlsanity.com/paper/2406.09015/) (2024)
- [Vision Transformer with Key-select Routing Attention for Single Image Dehazing](https://mlsanity.com/paper/2406.19703/) (2024)
- [Multi-scale Frequency Enhancement Network for Blind Image Deblurring](https://mlsanity.com/paper/2411.06893/) (2024)
- [Wavelet-Enhanced Desnowing: A Novel Single Image Restoration Approach for Traffic Surveillance under Adverse Weather Conditions](https://mlsanity.com/paper/2503.01339/) (2025)
- [Wavelet Policy: Imitation Learning in the Scale Domain with World Prior Memory](https://mlsanity.com/paper/2504.04991/) (2025)
- [D2-Mamba: Dual-Scale Fusion and Dual-Path Scanning with SSMs for Shadow Removal](https://mlsanity.com/paper/2508.12750/) (2025)
- [Phase-Aware Wavelet-Based-Scattering Encoder-Decoder for Dense Predictions](https://mlsanity.com/paper/2605.24621/) (2026)

## Abstract (arXiv)

Image deblurring is a classical computer vision problem that aims to recover a sharp image from a blurred image. To solve this problem, existing methods apply the Encode-Decode architecture to design the complex networks to make a good performance. However, most of these methods use repeated up-sampling and down-sampling structures to expand the receptive field, which results in texture information loss during the sampling process and some of them design the multiple stages that lead to difficulties with convergence. Therefore, our model uses dilated convolution to enable the obtainment of the large receptive field with high spatial resolution. Through making full use of the different receptive fields, our method can achieve better performance. On this basis, we reduce the number of up-sampling and down-sampling and design a simple network structure. Besides, we propose a novel module using the wavelet transform, which effectively helps the network to recover clear high-frequency texture details. Qualitative and quantitative evaluations of real and synthetic datasets show that our deblurring method is comparable to existing algorithms in terms of performance with much lower training requirements. The source code and pre-trained models are available at https://github.com/FlyEgle/SDWNet.