Copertina

Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Kondo, Yuki, Ukita, Norimichi
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Computer Vision and Pattern Recognition Artificial Intelligence Image and Video Processing
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Test-time Cost-and-Quality Controllable Arbitrary-Scale Super-Resolution with Variable Fourier Components
di: Akita, Kazutoshi, et al.
Pubblicazione: (2024)

Undertrained Image Reconstruction for Realistic Degradation in Blind Image Super-Resolution
di: Ito, Ru, et al.
Pubblicazione: (2025)

Learning the Degradation Distribution for Blind Image Super-Resolution
di: Luo, Zhengxiong, et al.
Pubblicazione: (2022)

Deep Blind Super-Resolution for Satellite Video
di: Xiao, Yi, et al.
Pubblicazione: (2024)

PixelBoost: Leveraging Brownian Motion for Realistic-Image Super-Resolution
di: Mishra, Aradhana, et al.
Pubblicazione: (2025)

Blind Super Resolution with Reference Images and Implicit Degradation Representation
di: Do, Huu-Phu, et al.
Pubblicazione: (2025)

Federated Learning for Blind Image Super-Resolution
di: Moser, Brian B., et al.
Pubblicazione: (2024)

Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution
di: Yuan, Jiang, et al.
Pubblicazione: (2024)

Deep Learning Framework for Infrastructure Maintenance: Crack Detection and High-Resolution Imaging of Infrastructure Surfaces
di: Pawar, Nikhil M., et al.
Pubblicazione: (2025)

CDFormer:When Degradation Prediction Embraces Diffusion Model for Blind Image Super-Resolution
di: Liu, Qingguo, et al.
Pubblicazione: (2024)

Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution
di: Shi, Haohan, et al.
Pubblicazione: (2025)

Boosting Diffusion Guidance via Learning Degradation-Aware Models for Blind Super Resolution
di: Lu, Shao-Hao, et al.
Pubblicazione: (2025)

HAAT: Hybrid Attention Aggregation Transformer for Image Super-Resolution
di: Lai, Song-Jiang, et al.
Pubblicazione: (2024)

RobustSAM: Segment Anything Robustly on Degraded Images
di: Chen, Wei-Ting, et al.
Pubblicazione: (2024)

Image Super-Resolution with Taylor Expansion Approximation and Large Field Reception
di: Feng, Jiancong, et al.
Pubblicazione: (2024)

Adaptive Blind Super-Resolution Network for Spatial-Specific and Spatial-Agnostic Degradations
di: Wen, Weilei, et al.
Pubblicazione: (2025)

Involution and BSConv Multi-Depth Distillation Network for Lightweight Image Super-Resolution
di: Khatami-Rizi, Akram, et al.
Pubblicazione: (2025)

Deep Learning for Segmentation of Cracks in High-Resolution Images of Steel Bridges
di: Kompanets, Andrii, et al.
Pubblicazione: (2024)

REHRSeg: Unleashing the Power of Self-Supervised Super-Resolution for Resource-Efficient 3D MRI Segmentation
di: Song, Zhiyun, et al.
Pubblicazione: (2024)

MedSR-Impact: Transformer-Based Super-Resolution for Lung CT Segmentation, Radiomics, Classification, and Prognosis
di: Martell, Marc Boubnovski, et al.
Pubblicazione: (2025)

Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation
di: Tang, Zeyu, et al.
Pubblicazione: (2023)

LightBSR: Towards Lightweight Blind Super-Resolution via Discriminative Implicit Degradation Representation Learning
di: Yuan, Jiang, et al.
Pubblicazione: (2025)

Time-Aware One Step Diffusion Network for Real-World Image Super-Resolution
di: Zhang, Tianyi, et al.
Pubblicazione: (2025)

MPSI: Mamba enhancement model for pixel-wise sequential interaction Image Super-Resolution
di: He, Yuchun, et al.
Pubblicazione: (2024)

UP-CrackNet: Unsupervised Pixel-Wise Road Crack Detection via Adversarial Image Restoration
di: Ma, Nachuan, et al.
Pubblicazione: (2024)

SeCo-INR: Semantically Conditioned Implicit Neural Representations for Improved Medical Image Super-Resolution
di: Ekanayake, Mevan, et al.
Pubblicazione: (2024)

RARE-UNet: Resolution-Aligned Routing Entry for Adaptive Medical Image Segmentation
di: Albertsen, Simon Winther, et al.
Pubblicazione: (2025)

Learning Correction Errors via Frequency-Self Attention for Blind Image Super-Resolution
di: Sun, Haochen, et al.
Pubblicazione: (2024)

MRI Super-Resolution with Deep Learning: A Comprehensive Survey
di: Khateri, Mohammad, et al.
Pubblicazione: (2025)

Adaptive Super Resolution For One-Shot Talking-Head Generation
di: Song, Luchuan, et al.
Pubblicazione: (2024)

APISR: Anime Production Inspired Real-World Anime Super-Resolution
di: Wang, Boyang, et al.
Pubblicazione: (2024)

Textural-Perceptual Joint Learning for No-Reference Super-Resolution Image Quality Assessment
di: Liu, Yuqing, et al.
Pubblicazione: (2022)

GRNN:Recurrent Neural Network based on Ghost Features for Video Super-Resolution
di: Guo, Yutong
Pubblicazione: (2025)

A Classification-Aware Super-Resolution Framework for Ship Targets in SAR Imagery
di: Awais, Ch Muhammad, et al.
Pubblicazione: (2025)

DWA: Differential Wavelet Amplifier for Image Super-Resolution
di: Moser, Brian B., et al.
Pubblicazione: (2023)

Mutual Evidential Deep Learning for Medical Image Segmentation
di: He, Yuanpeng, et al.
Pubblicazione: (2025)

Towards Realistic Data Generation for Real-World Super-Resolution
di: Peng, Long, et al.
Pubblicazione: (2024)

Hipandas: Hyperspectral Image Joint Denoising and Super-Resolution by Image Fusion with the Panchromatic Image
di: Xu, Shuang, et al.
Pubblicazione: (2024)

Global Rice Multi-Class Segmentation Dataset (RiceSEG): A Comprehensive and Diverse High-Resolution RGB-Annotated Images for the Development and Benchmarking of Rice Segmentation Algorithms
di: Zhou, Junchi, et al.
Pubblicazione: (2025)

Unsupervised Image Super-Resolution Reconstruction Based on Real-World Degradation Patterns
di: Tie, Yiyang, et al.
Pubblicazione: (2025)