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title = {Test-time training for matching-based video object segmentation}, |
author = {Bertrand, Juliette and Kordopatis Zilos, Giorgos and Kalantidis, Yannis and Tolias, Giorgos}, |
journal = {Advances in Neural Information Processing Systems}, |
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author = {Liu, Yexin and Zhang, Weiming and Zhao, Guoyang and Zhu, Jinjing and Vasilakos, Athanasios V and Wang, Lin}, |
journal = {IEEE Transactions on Artificial Intelligence}, |
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number = {10}, |
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year = {2023}, |
publisher = {IEEE}, |
doi = {10.1109/tai.2023.3336611 }, |
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author = {Marie, Alban and Desnos, Karol and Morin, Luce and Zhang, Lu}, |
booktitle = {International Conference on Quality of Multimedia Experience}, |
pages = {1--6}, |
year = {2023}, |
organization = {IEEE}, |
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A Survey on Degraded Image Segmentation
A comprehensive survey on robust image segmentation under various degradation conditions
Abstract
Segmentation is the core of visual understanding — the foundation of Physical AI and World Model.
Image segmentation is a fundamental task in computer vision with wide-ranging applications. While deep learning models have achieved remarkable success under ideal conditions, their performance often degrades catastrophically when faced with real-world image corruptions. These corruptions span several key categories, including adverse weather (e.g., fog, rain, snow), challenging light (e.g., nighttime, low-light), digital artifacts from processing (e.g., compression, color jitter), various forms of blur (e.g., motion, defocus), and pervasive noise (e.g., sensor noise, speckle).
This survey provides a comprehensive and structured overview of the field of degraded image segmentation. We establish a detailed taxonomy of common image degradations impacting segmentation tasks. We review a wide array of datasets and benchmarks designed for evaluating robustness. Furthermore, we systematically analyze state-of-the-art methodologies, categorized by their core technical strategies: Domain Adaptation and Generalization, Joint Restoration-Segmentation techniques, and Multi-modal Fusion.
This survey is essential for autonomous driving, robotics, and real-world AI systems — the core pillars of Physical AI.
Degradation Examples
Examples of various image degradation types: weather, light, digital, blur, and noise.
Physical AI Applications
Degraded image segmentation is fundamental to Physical AI — enabling reliable perception for autonomous vehicles and robots in real-world environments.
Highlights
- 135+ papers systematically organized following the survey's methodology taxonomy
- 37 papers with open-source code are marked with code links
- Comprehensive coverage of 5 degradation categories: Weather, Light, Digital, Blur, Noise
- 3 main methodological strategies: Domain Adaptation/Generalization, Joint Restoration-Segmentation, Multi-modal Fusion
- Essential for Physical AI — the foundation of autonomous driving, robotics, and real-world AI systems
Paper List
Papers with available code are marked with a code link in the "Code" column.
1. Domain Adaptation & Generalization (DA/DG)
1.1 Adversarial Learning Approaches
| Title | Authors | Year | Venue | Code | BibTeX |
|---|---|---|---|---|---|
| ICDA: Illumination-Coupled Domain Adaptation Framework for Unsupervised Nighttim... | Dong et al. | 2023 | IJCAI | Code | BibTeX |
| A one-stage domain adaptation network with image alignment for unsupervised nigh... | Wu et al. | 2021 | PAMI | Code | BibTeX |
| Dual-branch teacher-student with noise-tolerant learning for domain adaptive nig... | Chen et al. | 2024 | Image and Vision Com... | - | BibTeX |
| Weakly supervised semantic segmentation for point cloud based on view-based adve... | Miao et al. | 2023 | Computers & Graphic... | - | BibTeX |
| All-weather road drivable area segmentation method based on CycleGAN | Jiqing et al. | 2023 | The Visual Computer | - | BibTeX |
| FISS GAN: A generative adversarial network for foggy image semantic segmentation | Liu et al. | 2021 | IEEE/CAA Journal of ... | - | BibTeX |
| Semantic segmentation with unsupervised domain adaptation under varying weather ... | Erkent et al. | 2020 | IEEE Robotics and Au... | - | BibTeX |
| Nighttime road scene parsing by unsupervised domain adaptation | Song et al. | 2020 | IEEE transactions on... | - | BibTeX |
| Heatnet: Bridging the day-night domain gap in semantic segmentation with thermal... | Vertens et al. | 2020 | 2020 IEEE/RSJ Intern... | - | BibTeX |
| Advent: Adversarial entropy minimization for domain adaptation in semantic segme... | Vu et al. | 2019 | CVPR | - | BibTeX |
1.2 Feature Alignment
| Title | Authors | Year | Venue | Code | BibTeX |
|---|---|---|---|---|---|
| Condition-invariant semantic segmentation | Sakaridis et al. | 2025 | PAMI | Code | BibTeX |
| Contrastive model adaptation for cross-condition robustness in semantic segmenta... | Br{""u et al. | 2023 | ICCV | Code | BibTeX |
| Degraded Image Semantic Segmentation Using Intra-image and Inter-image Contrasti... | Dong et al. | 2023 | China Automation Con... | Code | BibTeX |
| Cross-domain correlation distillation for unsupervised domain adaptation in nigh... | Gao et al. | 2022 | CVPR | Code | BibTeX |
| Computational Imaging for Machine Perception: Transferring Semantic Segmentation... | Jiang et al. | 2024 | IEEE Transactions on... | - | BibTeX |
| Refign: Align and refine for adaptation of semantic segmentation to adverse cond... | Br{""u et al. | 2023 | WACV | - | BibTeX |
| Fifo: Learning fog-invariant features for foggy scene segmentation. | Lee et al. | 2022 | CVPR | - | BibTeX |
| Cluster alignment with target knowledge mining for unsupervised domain adaptatio... | Wang et al. | 2022 | IEEE Transactions on... | - | BibTeX |
| Learning intra-domain style-invariant representation for unsupervised domain ada... | Li et al. | 2022 | Pattern Recognition | - | BibTeX |
| Semantic nighttime image segmentation via illumination and position aware domain... | Peng et al. | 2021 | 2021 IEEE Internatio... | - | BibTeX |
| Learning texture invariant representation for domain adaptation of semantic segm... | Kim et al. | 2020 | CVPR | - | BibTeX |
| Degraded image semantic segmentation with dense-gram networks | Guo et al. | 2019 | IEEE Transactions on... | - | BibTeX |
| Ssf-dan: Separated semantic feature based domain adaptation network for semantic... | Du et al. | 2019 | ICCV | - | BibTeX |
1.3 Feature Decomposition
| Title | Authors | Year | Venue | Code | BibTeX |
|---|---|---|---|---|---|
| Learning generalized segmentation for foggy-scenes by bi-directional wavelet gui... | Bi et al. | 2024 | the AAAI Conference ... | Code | BibTeX |
| When semantic segmentation meets frequency aliasing | Chen et al. | 2024 | ICLR | Code | BibTeX |
| All about structure: Adapting structural information across domains for boosting... | Chang et al. | 2019 | CVPR | Code | BibTeX |
| Generalized Foggy-Scene Semantic Segmentation by Frequency Decoupling | Bi et al. | 2024 | CVPR | - | BibTeX |
| DDFL: Dual-Domain Feature Learning for nighttime semantic segmentation | Lin et al. | 2024 | Displays | - | BibTeX |
| Disentangle then Parse: Night-time Semantic Segmentation with Illumination Disen... | Wei et al. | 2023 | ICCV | - | BibTeX |
| Interactive learning of intrinsic and extrinsic properties for all-day semantic ... | Bi et al. | 2023 | IEEE Transactions on... | - | BibTeX |
| Both style and fog matter: Cumulative domain adaptation for semantic foggy scene... | Ma et al. | 2022 | CVPR | - | BibTeX |
1.4 Self-Training & Pseudo-Labeling
| Title | Authors | Year | Venue | Code | BibTeX |
|---|---|---|---|---|---|
| Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation | Zhao et al. | 2024 | CVPR | Code | BibTeX |
| Self pseudo entropy knowledge distillation for semi-supervised semantic segmenta... | Lu et al. | 2024 | IEEE Transactions on... | Code | BibTeX |
| VBLC: Visibility boosting and logit-constraint learning for domain adaptive sema... | Li et al. | 2023 | the AAAI Conference ... | Code | BibTeX |
| LoopDA: Constructing self-loops to adapt nighttime semantic segmentation | Shen et al. | 2023 | WACV | Code | BibTeX |
| Dtbs: Dual-teacher bi-directional self-training for domain adaptation in nightti... | Huang et al. | 2023 | European Conference ... | Code | BibTeX |
| Online domain adaptation for semantic segmentation in ever-changing conditions | Panagiotakopoulos et al. | 2022 | European Conference ... | Code | BibTeX |
| Bidirectional learning for domain adaptation of semantic segmentation | Li et al. | 2019 | CVPR | Code | BibTeX |
| Source-Free Online Domain Adaptive Semantic Segmentation of Satellite Images Und... | Niloy et al. | 2024 | ICASSP 2024-2024 IEE... | - | BibTeX |
| SDAT-Former++: A Foggy Scene Semantic Segmentation Method with Stronger Domain A... | Wang et al. | 2023 | Remote Sensing | - | BibTeX |
| A Two-Stage Self-Training Framework for Nighttime Semantic Segmentation | Yang et al. | 2023 | 2023 38th Youth Acad... | - | BibTeX |
| SGDA: A Saliency-Guided Domain Adaptation Network for Nighttime Semantic Segment... | Duan et al. | 2023 | 2023 IEEE 6th Intern... | - | BibTeX |
| Dual-level Consistency Learning for Unsupervised Domain Adaptive Night-time Sema... | Ding et al. | 2023 | 2023 IEEE Internatio... | - | BibTeX |
| MADA: Multi-Level Alignment in Domain Adaptation Network for Nighttime Semantic ... | Xu et al. | 2023 | 2023 8th Internation... | - | BibTeX |
| A hybrid domain learning framework for unsupervised semantic segmentation | Zhang et al. | 2023 | Neurocomputing | - | BibTeX |
| FogAdapt: Self-supervised domain adaptation for semantic segmentation of foggy i... | Iqbal et al. | 2022 | Neurocomputing | - | BibTeX |
| Unsupervised foggy scene understanding via self spatial-temporal label diffusion | Liao et al. | 2022 | IEEE Transactions on... | - | BibTeX |
| Augmentation consistency-guided self-training for source-free domain adaptive se... | Prabhu et al. | 2022 | NeurIPS 2022 Worksho... | - | BibTeX |
| SS-SFDA: Self-supervised source-free domain adaptation for road segmentation in ... | Kothandaraman et al. | 2021 | ICCV | - | BibTeX |
| CDAda: A curriculum domain adaptation for nighttime semantic segmentation | Xu et al. | 2021 | ICCV | - | BibTeX |
| RanPaste: Paste consistency and pseudo label for semisupervised remote sensing i... | Wang et al. | 2021 | IEEE Transactions on... | - | BibTeX |
1.5 Knowledge Distillation
| Title | Authors | Year | Venue | Code | BibTeX |
|---|---|---|---|---|---|
| Lightweight deep learning methods for panoramic dental X-ray image segmentation | Lin et al. | 2023 | Neural Computing and... | Code | BibTeX |
| Weather-degraded image semantic segmentation with multi-task knowledge distillat... | Li et al. | 2022 | Image and Vision Com... | - | BibTeX |
| Self-feature distillation with uncertainty modeling for degraded image recogniti... | Yang et al. | 2022 | European Conference ... | - | BibTeX |
| Robust semantic segmentation with multi-teacher knowledge distillation | Amirkhani et al. | 2021 | IEEE Access | - | BibTeX |
| Efficient uncertainty estimation in semantic segmentation via distillation | Holder et al. | 2021 | ICCV | - | BibTeX |
1.6 Test-Time Adaptation & Continual Learning
| Title | Authors | Year | Venue | Code | BibTeX |
|---|---|---|---|---|---|
| Privacy-Preserving Synthetic Continual Semantic Segmentation for Robotic Surgery | Xu et al. | 2024 | IEEE Transactions on... | Code | BibTeX |
| Enhanced Model Robustness to Input Corruptions by Per-corruption Adaptation of N... | Camuffo et al. | 2024 | 2024 IEEE/RSJ Intern... | - | BibTeX |
| Test-time adaptation for nighttime color-thermal semantic segmentation | Liu et al. | 2023 | IEEE Transactions on... | - | BibTeX |
| Test-time training for matching-based video object segmentation | Bertrand et al. | 2023 | Advances in Neural I... | - | BibTeX |
| Top-K Confidence Map Aggregation for Robust Semantic Segmentation Against Unexpe... | Moriyasu et al. | 2023 | 2023 IEEE Internatio... | - | BibTeX |
| Principles of forgetting in domain-incremental semantic segmentation in adverse ... | Kalb et al. | 2023 | CVPR | - | BibTeX |
| Rethinking exemplars for continual semantic segmentation in endoscopy scenes: En... | Wang et al. | 2023 | Computers in Biology... | - | BibTeX |
| To adapt or not to adapt? real-time adaptation for semantic segmentation | Colomer et al. | 2023 | ICCV | - | BibTeX |
| Continual test-time domain adaptation | Wang et al. | 2022 | CVPR | - | BibTeX |
| Continual unsupervised domain adaptation for semantic segmentation using a class... | Marsden et al. | 2022 | 2022 International J... | - | BibTeX |
1.7 Other DA/DG Strategies
| Title | Authors | Year | Venue | Code | BibTeX |
|---|---|---|---|---|---|
| A Re-Parameterized Vision Transformer (ReVT) for Domain-Generalized Semantic Seg... | Term{""o et al. | 2023 | ICCV | Code | BibTeX |
| Map-guided curriculum domain adaptation and uncertainty-aware evaluation for sem... | Sakaridis et al. | 2020 | PAMI | Code | BibTeX |
| Complementary Masked-Guided Meta-Learning for Domain Adaptive Nighttime Segmenta... | Chen et al. | 2024 | IEEE Signal Processi... | - | BibTeX |
| CAT: Exploiting Inter-Class Dynamics for Domain Adaptive Object Detection | Kennerley et al. | 2024 | CVPR | - | BibTeX |
| Learning to learn single domain generalization | Qiao et al. | 2020 | CVPR | - | BibTeX |
| Curriculum model adaptation with synthetic and real data for semantic foggy scen... | Dai et al. | 2020 | International Journa... | - | BibTeX |
| Guided curriculum model adaptation and uncertainty-aware evaluation for semantic... | Sakaridis et al. | 2019 | ICCV | - | BibTeX |
| Model adaptation with synthetic and real data for semantic dense foggy scene und... | Sakaridis et al. | 2018 | ECCV | - | BibTeX |
2. Joint Restoration & Segmentation
2.1 Dehazing/Defogging + Segmentation
| Title | Authors | Year | Venue | Code | BibTeX |
|---|---|---|---|---|---|
| Improving semantic segmentation under hazy weather for autonomous vehicles using... | Saravanarajan et al. | 2023 | IEEE Access | - | BibTeX |
| Budget-Aware Road Semantic Segmentation in Unseen Foggy Scenes | To et al. | 2023 | International Confer... | - | BibTeX |
| Rethinking image restoration for object detection | Sun et al. | 2022 | Advances in Neural I... | - | BibTeX |
| Cooperative semantic segmentation and image restoration in adverse environmental... | Xia et al. | 2019 | arXiv preprint arXiv... | - | BibTeX |
| A convolutional network for joint deraining and dehazing from a single image for... | Sun et al. | 2019 | 2019 IEEE/RSJ Intern... | - | BibTeX |
2.2 Deraining + Segmentation
| Title | Authors | Year | Venue | Code | BibTeX |
|---|---|---|---|---|---|
| DRNet: Learning a dynamic recursion network for chaotic rain streak removal | Jiang et al. | 2025 | Pattern Recognition | Code | BibTeX |
| Rainy day image semantic segmentation based on two-stage progressive network | Zhang et al. | 2024 | The Visual Computer | Code | BibTeX |
| RCDNet: An interpretable rain convolutional dictionary network for single image ... | Wang et al. | 2023 | IEEE Transactions on... | Code | BibTeX |
| SAPNet: Segmentation-aware progressive network for perceptual contrastive derain... | Zheng et al. | 2022 | WACV | Code | BibTeX |
| Beyond monocular deraining: Parallel stereo deraining network via semantic prior | Zhang et al. | 2022 | International Journa... | Code | BibTeX |
| Towards robust rain removal against adversarial attacks: A comprehensive benchma... | Yu et al. | 2022 | CVPR | Code | BibTeX |
| A De-raining semantic segmentation network for real-time foreground segmentation | Wang et al. | 2021 | Journal of Real-Time... | Code | BibTeX |
| Style Optimization Networks for real-time semantic segmentation of rainy and fog... | Huang et al. | 2025 | Signal Processing: I... | - | BibTeX |
| Learning A Rain-Invariant Network For Instance Segmentation In The Rain | Chen et al. | 2024 | 2024 IEEE Internatio... | - | BibTeX |
| Real rainy scene analysis: A dual-module benchmark for image deraining and segme... | Zhao et al. | 2023 | 2023 IEEE Internatio... | - | BibTeX |
| Improved sea-ice identification using semantic segmentation with raindrop remova... | Alsharay et al. | 2022 | IEEE Access | - | BibTeX |
| I can see clearly now: Image restoration via de-raining | Porav et al. | 2019 | ICRA | - | BibTeX |
2.3 Denoising + Segmentation
| Title | Authors | Year | Venue | Code | BibTeX |
|---|---|---|---|---|---|
| Instance Segmentation in the Dark | Chen et al. | 2023 | IJCV | Code | BibTeX |
| AATCT-IDS: A benchmark Abdominal Adipose Tissue CT Image Dataset for image denoi... | Ma et al. | 2024 | Computers in Biology... | - | BibTeX |
| Multi task deep learning phase unwrapping method based on semantic segmentation | Wang et al. | 2024 | Journal of Optics | - | BibTeX |
| Plug-and-Play Joint Image Deblurring and Detection | Marrs et al. | 2023 | 2023 IEEE 25th Inter... | - | BibTeX |
| Segmentation-guided semantic-aware self-supervised denoising for SAR image | Yuan et al. | 2023 | IEEE Transactions on... | - | BibTeX |
| Denoising pretraining for semantic segmentation | Brempong et al. | 2022 | CVPR_Workshops | - | BibTeX |
| Speckle reduction via deep content-aware image prior for precise breast tumor se... | Lee et al. | 2022 | IEEE Transactions on... | - | BibTeX |
| Efnet: Enhancement-fusion network for semantic segmentation | Wang et al. | 2021 | Pattern Recognition | - | BibTeX |
| Effective image restoration for semantic segmentation | Niu et al. | 2020 | Neurocomputing | - | BibTeX |
| Dapas: Denoising autoencoder to prevent adversarial attack in semantic segmentat... | Cho et al. | 2020 | 2020 International J... | - | BibTeX |
| Improved denoising autoencoder for maritime image denoising and semantic segment... | Qiu et al. | 2020 | China Communications | - | BibTeX |
| DN-GAN: Denoising generative adversarial networks for speckle noise reduction in... | Chen et al. | 2020 | Biomedical Signal Pr... | - | BibTeX |
2.4 Deblurring + Segmentation
| Title | Authors | Year | Venue | Code | BibTeX |
|---|---|---|---|---|---|
| Turb-Seg-Res: A Segment-then-Restore Pipeline for Dynamic Videos with Atmospheri... | Saha et al. | 2024 | CVPR | - | BibTeX |
| Automatic extraction of blur regions on a single image based on semantic segment... | Shen et al. | 2020 | IEEE Access | - | BibTeX |
| Joint stereo video deblurring, scene flow estimation and moving object segmentat... | Pan et al. | 2019 | IEEE Transactions on... | - | BibTeX |
| From motion blur to motion flow: A deep learning solution for removing heterogen... | Gong et al. | 2017 | CVPR | - | BibTeX |
2.5 Snow/Dust Removal + Segmentation
| Title | Authors | Year | Venue | Code | BibTeX |
|---|---|---|---|---|---|
| Deep dense multi-scale network for snow removal using semantic and depth priors | Zhang et al. | 2021 | IEEE Transactions on... | Code | BibTeX |
| Semantic Segmentation and Inpainting of Dust with the S-Dust Dataset | Buckel et al. | 2023 | International Federa... | - | BibTeX |
2.6 Low-Light Enhancement + Segmentation
| Title | Authors | Year | Venue | Code | BibTeX |
|---|---|---|---|---|---|
| Nighttime image semantic segmentation with retinex theory | Sun et al. | 2024 | Image and Vision Com... | Code | BibTeX |
| Improving nighttime driving-scene segmentation via dual image-adaptive learnable... | Liu et al. | 2023 | IEEE Transactions on... | Code | BibTeX |
| Toward fast, flexible, and robust low-light image enhancement | Ma et al. | 2022 | CVPR | Code | BibTeX |
| Lane detection based on real-time semantic segmentation for end-to-end autonomou... | Liu et al. | 2024 | Digital Signal Proce... | - | BibTeX |
| Towards learning low-light indoor semantic segmentation with illumination-invari... | Zhang et al. | 2021 | The International Ar... | - | BibTeX |
2.7 JPEG Decoding + Segmentation
| Title | Authors | Year | Venue | Code | BibTeX |
|---|---|---|---|---|---|
| DSSLIC: Deep semantic segmentation-based layered image compression | Akbari et al. | 2019 | IEEE International C... | Code | BibTeX |
| DCT-CompSegNet: fast layout segmentation in DCT compressed JPEG document images ... | Rajesh et al. | 2024 | Multimedia Tools and... | - | BibTeX |
| Semantic segmentation in learned compressed domain | Liu et al. | 2022 | Picture Coding Sympo... | - | BibTeX |
| Reverse error modeling for improved semantic segmentation | Kuhn et al. | 2022 | IEEE International C... | - | BibTeX |
| Deep learning based image segmentation directly in the jpeg compressed domain | Singh et al. | 2021 | 2021 IEEE 8th Uttar ... | - | BibTeX |
| Semantic segmentation of JPEG blocks using a deep CNN for non-aligned JPEG forge... | Alipour et al. | 2020 | Multimedia Tools and... | - | BibTeX |
3. Multi-Modal Fusion
3.1 RGB + Thermal Fusion
| Title | Authors | Year | Venue | Code | BibTeX |
|---|---|---|---|---|---|
| MCNet: Multi-level correction network for thermal image semantic segmentation of... | Xiong et al. | 2021 | Infrared Physics & ... | Code | BibTeX |
| Illumination Robust Semantic Segmentation Based on Cross-dimensional Multispectr... | Ni et al. | 2024 | IEEE Access | - | BibTeX |
| CCAFFMNet: Dual-spectral semantic segmentation network with channel-coordinate a... | Yi et al. | 2022 | Neurocomputing | - | BibTeX |
| FuseSeg: Semantic segmentation of urban scenes based on RGB and thermal data fus... | Sun et al. | 2020 | IEEE Transactions on... | - | BibTeX |
| Robust semantic segmentation in adverse weather conditions by means of sensor da... | Pfeuffer et al. | 2019 | International Confer... | - | BibTeX |
| RTFNet: RGB-thermal fusion network for semantic segmentation of urban scenes | Sun et al. | 2019 | IEEE Robotics and Au... | - | BibTeX |
3.2 RGB + LiDAR/Depth Fusion
| Title | Authors | Year | Venue | Code | BibTeX |
|---|---|---|---|---|---|
| Low-Light Enhancement and Global-Local Feature Interaction for RGB-T Semantic Se... | Guo et al. | 2025 | IEEE Transactions on... | Code | BibTeX |
| Adaptive Entropy Multi-modal Fusion for Nighttime Lane Segmentation | Zhang et al. | 2024 | IEEE Transactions on... | - | BibTeX |
| Delivering arbitrary-modal semantic segmentation | Zhang et al. | 2023 | CVPR | - | BibTeX |
| Multi-robot collaborative perception with graph neural networks | Zhou et al. | 2022 | IEEE Robotics and Au... | - | BibTeX |
| Multi-modal sensor fusion-based semantic segmentation for snow driving scenarios | Vachmanus et al. | 2021 | IEEE sensors journal | - | BibTeX |
| UNO: Uncertainty-aware noisy-or multimodal fusion for unanticipated input degrad... | Tian et al. | 2020 | ICRA | - | BibTeX |
3.3 RGB + Event Camera Fusion
| Title | Authors | Year | Venue | Code | BibTeX |
|---|---|---|---|---|---|
| Event-assisted low-light video object segmentation | Li et al. | 2024 | CVPR | Code | BibTeX |
| Cmda: Cross-modality domain adaptation for nighttime semantic segmentation | Xia et al. | 2023 | ICCV | Code | BibTeX |
| Semantic Segmentation Research of Motion Blurred Images by Event Camera | Liu et al. | 2023 | International Confer... | - | BibTeX |
Citation
If you find this survey helpful, please cite:
@article{chen2026degraded,
title={A Survey on Degraded Image Segmentation},
author={Chen, Linwei and Fu, Ying and Shangguan, Jingyu and Xu, Jinglin and Peng, Yuxin},
journal={Chinese Journal of Electronics},
year={2026}
}
Contributing
We welcome contributions! Please feel free to submit a Pull Request to add new papers or fix any issues.
Contact
- Linwei Chen - chenlinwei.ai@gmail.com
- Ying Fu (Corresponding Author) - fuying@bit.edu.cn
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