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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.
Intra and Inter Parser-Prompted Transformers for Effective Image Restoration
SIR-DIFF: Sparse Image Sets Restoration with Multi-View Diffusion Model
Deblur Gaussian Splatting SLAM
Simulating Dual-Pixel Images From Ray Tracing For Depth Estimation
Reconstruct Anything Model: a lightweight foundation model for computational imaging
One-Step Diffusion Model for Image Motion-Deblurring
DarkDeblur: Learning single-shot image deblurring in low-light condition
Near-infrared Image Deblurring and Event Denoising with Synergistic Neuromorphic Imaging
Towards Differential Handling of Various Blur Regions for Accurate Image Deblurring
Self-supervised conformal prediction for uncertainty quantification in Poisson imaging problems
Spatial and Frequency Domain Adaptive Fusion Network for Image Deblurring
Exploiting Deblurring Networks for Radiance Fields
A Physics-Informed Blur Learning Framework for Imaging Systems
Adjust Your Focus: Defocus Deblurring From Dual-Pixel Images Using Explicit Multi-Scale Cross-Correlation
An Improved Optimal Proximal Gradient Algorithm for Non-Blind Image Deblurring
Self-supervised Conformal Prediction for Uncertainty Quantification in Imaging Problems
Unpaired Deblurring via Decoupled Diffusion Model
EENet: Frequency-Aware and Spatially Multiscale Network for Single Image Dehazing
Multi-Frame Blind Manifold Deconvolution for Rotating Synthetic Aperture Imaging
Efficient Transformer for High Resolution Image Motion Deblurring
ClearSight: Human Vision-Inspired Solutions for Event-Based Motion Deblurring
Image Motion Blur Removal in the Temporal Dimension with Video Diffusion Models
Video Deblurring by Sharpness Prior Detection and Edge Information
DiffStereo: High-Frequency Aware Diffusion Model for Stereo Image Restoration
Soft Knowledge Distillation with Multi-Dimensional Cross-Net Attention for Image Restoration Models Compression
Joint Transmission and Deblurring: A Semantic Communication Approach Using Events
Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks
MB-TaylorFormer V2: Improved Multi-branch Linear Transformer Expanded by Taylor Formula for Image Restoration
Conditional Mutual Information Based Diffusion Posterior Sampling for Solving Inverse Problems
Zero-Shot Image Restoration Using Few-Step Guidance of Consistency Models (and Beyond)
Enhancing Diffusion Models for Inverse Problems with Covariance-Aware Posterior Sampling
MaIR: A Locality- and Continuity-Preserving Mamba for Image Restoration
Generative Adversarial Network on Motion-Blur Image Restoration
Solving Inverse Problems via Diffusion Optimal Control
DarkIR: Robust Low-Light Image Restoration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.