# Unsupervised Domain-Specific Deblurring via Disentangled Representations

arXiv: https://arxiv.org/abs/1903.01594 · HTML: https://mlsanity.com/paper/1903.01594/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2019.

## Built on by (most cited first, 30 of 37)

- [GAN Inversion: A Survey](https://mlsanity.com/paper/2101.05278/) (2021)
- [Deep Image Deblurring: A Survey](https://mlsanity.com/paper/2201.10700/) (2022)
- [Multiscale Structure Guided Diffusion for Image Deblurring](https://mlsanity.com/paper/2212.01789/) (2022)
- [NTIRE 2021 Challenge on Image Deblurring](https://mlsanity.com/paper/2104.14854/) (2021)
- [CoMoGAN: continuous model-guided image-to-image translation](https://mlsanity.com/paper/2103.06879/) (2021)
- [MC-Blur: A Comprehensive Benchmark for Image Deblurring](https://mlsanity.com/paper/2112.00234/) (2021)
- [FCL-GAN: A Lightweight and Real-Time Baseline for Unsupervised Blind Image Deblurring](https://mlsanity.com/paper/2204.07820/) (2022)
- [Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach](https://mlsanity.com/paper/2204.10090/) (2022)
- [Learning to Refactor Action and Co-occurrence Features for Temporal Action Localization](https://mlsanity.com/paper/2206.11493/) (2022)
- [Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior](https://mlsanity.com/paper/2112.01766/) (2021)
- [Blur2Blur: Blur Conversion for Unsupervised Image Deblurring on Unknown Domains](https://mlsanity.com/paper/2403.16205/) (2024)
- [Harnessing Unrecognizable Faces for Improving Face Recognition](https://mlsanity.com/paper/2106.04112/) (2021)
- [HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration](https://mlsanity.com/paper/2107.02660/) (2021)
- [DeepStroke: An Efficient Stroke Screening Framework for Emergency Rooms with Multimodal Adversarial Deep Learning](https://mlsanity.com/paper/2109.12065/) (2021)
- [Blind Motion Deblurring Super-Resolution: When Dynamic Spatio-Temporal Learning Meets Static Image Understanding](https://mlsanity.com/paper/2105.13077/) (2021)
- [Interactive Feature Embedding for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2211.04877/) (2022)
- [Federated CycleGAN for Privacy-Preserving Image-to-Image Translation](https://mlsanity.com/paper/2106.09246/) (2021)
- [Blind Image Deconvolution Using Variational Deep Image Prior](https://mlsanity.com/paper/2202.00179/) (2022)
- [ELMformer: Efficient Raw Image Restoration with a Locally Multiplicative Transformer](https://mlsanity.com/paper/2208.14704/) (2022)
- [Disentangled Representations for Short-Term and Long-Term Person Re-Identification](https://mlsanity.com/paper/2409.05277/) (2024)
- [Language-Guided Visual Perception Disentanglement for Image Quality Assessment and Conditional Image Generation](https://mlsanity.com/paper/2503.02206/) (2025)
- [Semi-UFormer: Semi-supervised Uncertainty-aware Transformer for Image Dehazing](https://mlsanity.com/paper/2210.16057/) (2022)
- [Blind Non-Uniform Motion Deblurring using Atrous Spatial Pyramid Deformable Convolution and Deblurring-Reblurring Consistency](https://mlsanity.com/paper/2106.14336/) (2021)
- [LODE: Deep Local Deblurring and A New Benchmark](https://mlsanity.com/paper/2109.09149/) (2021)
- [Network Architecture Search for Face Enhancement](https://mlsanity.com/paper/2105.06528/) (2021)
- [Edge Prior Augmented Networks for Motion Deblurring on Naturally Blurry Images](https://mlsanity.com/paper/2109.08915/) (2021)
- [A survey on facial image deblurring](https://mlsanity.com/paper/2302.05017/) (2023)
- [TabDeco: A Comprehensive Contrastive Framework for Decoupled Representations in Tabular Data](https://mlsanity.com/paper/2411.11148/) (2024)
- [Learning Deblurring Texture Prior from Unpaired Data with Diffusion Model](https://mlsanity.com/paper/2507.13599/) (2025)
- [DUET: Detection Utilizing Enhancement for Text in Scanned or Captured Documents](https://mlsanity.com/paper/2106.05542/) (2021)

## Abstract (arXiv)

Image deblurring aims to restore the latent sharp images from the corresponding blurred ones. In this paper, we present an unsupervised method for domain-specific single-image deblurring based on disentangled representations. The disentanglement is achieved by splitting the content and blur features in a blurred image using content encoders and blur encoders. We enforce a KL divergence loss to regularize the distribution range of extracted blur attributes such that little content information is contained. Meanwhile, to handle the unpaired training data, a blurring branch and the cycle-consistency loss are added to guarantee that the content structures of the deblurred results match the original images. We also add an adversarial loss on deblurred results to generate visually realistic images and a perceptual loss to further mitigate the artifacts. We perform extensive experiments on the tasks of face and text deblurring using both synthetic datasets and real images, and achieve improved results compared to recent state-of-the-art deblurring methods.