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Denoising
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Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.
<span style="color:grey; opacity: 0.6">( Image credit: Beyond a Gaussian Denoiser )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Efficient Medical Image Restoration via Reliability Guided Learning in Frequency Domain
ADT: Tuning Diffusion Models with Adversarial Supervision
Predicting Wave Dynamics using Deep Learning with Multistep Integration Inspired Attention and Physics-Based Loss Decomposition
Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual Perception
Score Matching Diffusion Based Feedback Control and Planning of Nonlinear Systems
Separate to Collaborate: Dual-Stream Diffusion Model for Coordinated Piano Hand Motion Synthesis
Prior Does Matter: Visual Navigation via Denoising Diffusion Bridge Models
VibrantLeaves: A principled parametric image generator for training deep restoration models
DiffMOD: Progressive Diffusion Point Denoising for Moving Object Detection in Remote Sensing
Invariance Matters: Empowering Social Recommendation via Graph Invariant Learning
Decoupled Diffusion Sparks Adaptive Scene Generation
H3AE: High Compression, High Speed, and High Quality AutoEncoder for Video Diffusion Models
Ising Models with Hidden Markov Structure: Applications to Probabilistic Inference in Machine Learning
Computationally iterative methods for salt-and-pepper denoising
Mitigating Long-tail Distribution in Oracle Bone Inscriptions: Dataset, Model, and Benchmark
Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR << 1 Imaging
AB-Cache: Training-Free Acceleration of Diffusion Models via Adams-Bashforth Cached Feature Reuse
UniFlowRestore: A General Video Restoration Framework via Flow Matching and Prompt Guidance
seg2med: a bridge from artificial anatomy to multimodal medical images
Training-free Guidance in Text-to-Video Generation via Multimodal Planning and Structured Noise Initialization
Novel Diffusion Models for Multimodal 3D Hand Trajectory Prediction
ID-Booth: Identity-consistent Face Generation with Diffusion Models
Emergency Communication: OTFS-Based Semantic Transmission with Diffusion Noise Suppression
Unifying and extending Diffusion Models through PDEs for solving Inverse Problems
V2V3D: View-to-View Denoised 3D Reconstruction for Light-Field Microscopy
Zero-Shot Low-dose CT Denoising via Sinogram Flicking
MoEDiff-SR: Mixture of Experts-Guided Diffusion Model for Region-Adaptive MRI Super-Resolution
DDT: Decoupled Diffusion Transformer
Under-Sampled High-Dimensional Data Recovery via Symbiotic Multi-Prior Tensor Reconstruction
OSDM-MReg: Multimodal Image Registration based One Step Diffusion Model
Physical spline for denoising object trajectory data by combining splines, ML feature regression and model knowledge
Releasing Differentially Private Event Logs Using Generative Models
AstroClearNet: Deep image prior for multi-frame astronomical image restoration
Examining Joint Demosaicing and Denoising for Single-, Quad-, and Nona-Bayer Patterns
REWIND: Real-Time Egocentric Whole-Body Motion Diffusion with Exemplar-Based Identity Conditioning
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