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Deblurring
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Deblurring is a computer vision task that involves removing the blurring artifacts from images or videos to restore the original, sharp content. Blurring can be caused by various factors such as camera shake, fast motion, and out-of-focus objects, and can result in a loss of detail and quality in the captured images. The goal of deblurring is to produce a clear, high-quality image that accurately represents the original scene.
<span style="color:grey; opacity: 0.6">( Image credit: Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Generative Latent Kernel Modeling for Blind Motion Deblurring
EAMamba: Efficient All-Around Vision State Space Model for Image Restoration
Dynamic Bandwidth Allocation for Hybrid Event-RGB Transmission
Visual-Instructed Degradation Diffusion for All-in-One Image Restoration
R3eVision: A Survey on Robust Rendering, Restoration, and Enhancement for 3D Low-Level Vision
Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching
Restoring Gaussian Blurred Face Images for Deanonymization Attacks
Plug-and-Play Linear Attention for Pre-trained Image and Video Restoration Models
Multi-Step Guided Diffusion for Image Restoration on Edge Devices: Toward Lightweight Perception in Embodied AI
EV-LayerSegNet: Self-supervised Motion Segmentation using Event Cameras
On Inverse Problems, Parameter Estimation, and Domain Generalization
Efficient RAW Image Deblurring with Adaptive Frequency Modulation
URWKV: Unified RWKV Model with Multi-state Perspective for Low-light Image Restoration
Plug-and-Play Posterior Sampling for Blind Inverse Problems
SpikeGen: Generative Framework for Visual Spike Stream Processing
Deep Learning-Driven Ultra-High-Definition Image Restoration: A Survey
Joint Flow And Feature Refinement Using Attention For Video Restoration
Linear Convergence of Plug-and-Play Algorithms with Kernel Denoisers
Degradation-Aware Feature Perturbation for All-in-One Image Restoration
UGoDIT: Unsupervised Group Deep Image Prior Via Transferable Weights
An incremental algorithm for non-convex AI-enhanced medical image processing
Image Restoration via Multi-domain Learning
MoBGS: Motion Deblurring Dynamic 3D Gaussian Splatting for Blurry Monocular Video
Frequency-domain Learning with Kernel Prior for Blind Image Deblurring
Event-Enhanced Blurry Video Super-Resolution
Coding-Prior Guided Diffusion Network for Video Deblurring
NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results
EBAD-Gaussian: Event-driven Bundle Adjusted Deblur Gaussian Splatting
X-DECODE: EXtreme Deblurring with Curriculum Optimization and Domain Equalization
AstroClearNet: Deep image prior for multi-frame astronomical image restoration
DiET-GS: Diffusion Prior and Event Stream-Assisted Motion Deblurring 3D Gaussian Splatting
RELD: Regularization by Latent Diffusion Models for Image Restoration
Quantile-Based Randomized Kaczmarz for Corrupted Tensor Linear Systems
Fractal-IR: A Unified Framework for Efficient and Scalable Image Restoration
BARD-GS: Blur-Aware Reconstruction of Dynamic Scenes via Gaussian Splatting
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