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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.
Consistent World Models via Foresight Diffusion
SHaDe: Compact and Consistent Dynamic 3D Reconstruction via Tri-Plane Deformation and Latent Diffusion
REPA Works Until It Doesn't: Early-Stopped, Holistic Alignment Supercharges Diffusion Training
Training-Free Efficient Video Generation via Dynamic Token Carving
Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation
Creatively Upscaling Images with Global-Regional Priors
Pursuing Temporal-Consistent Video Virtual Try-On via Dynamic Pose Interaction
Sufficient conditions for offline reactivation in recurrent neural networks
Guided Diffusion Sampling on Function Spaces with Applications to PDEs
Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering
Non-rigid Motion Correction for MRI Reconstruction via Coarse-To-Fine Diffusion Models
DeepKD: A Deeply Decoupled and Denoised Knowledge Distillation Trainer
Decoding Phone Pairs from MEG Signals Across Speech Modalities
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off
dKV-Cache: The Cache for Diffusion Language Models
Adaptive Estimation and Learning under Temporal Distribution Shift
Diffusion Probabilistic Generative Models for Accelerated, in-NICU Permanent Magnet Neonatal MRI
Image-to-Image Translation with Diffusion Transformers and CLIP-Based Image Conditioning
Toward Theoretical Insights into Diffusion Trajectory Distillation via Operator Merging
Improving Noise Robustness of LLM-based Zero-shot TTS via Discrete Acoustic Token Denoising
Adaptive Cyclic Diffusion for Inference Scaling
AquaSignal: An Integrated Framework for Robust Underwater Acoustic Analysis
RefiDiff: Refinement-Aware Diffusion for Efficient Missing Data Imputation
Neural Inverse Scattering with Score-based Regularization
Time Series Similarity Score Functions to Monitor and Interact with the Training and Denoising Process of a Time Series Diffusion Model applied to a Human Activity Recognition Dataset based on IMUs
Communication-Efficient Diffusion Denoising Parallelization via Reuse-then-Predict Mechanism
Multi-Channel Swin Transformer Framework for Bearing Remaining Useful Life Prediction
Degradation-Aware Feature Perturbation for All-in-One Image Restoration
AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use
Anti-Inpainting: A Proactive Defense against Malicious Diffusion-based Inpainters under Unknown Conditions
Stochastic Orthogonal Regularization for deep projective priors
Higher fidelity perceptual image and video compression with a latent conditioned residual denoising diffusion model
FlowPure: Continuous Normalizing Flows for Adversarial Purification
Denoising Diffusion Probabilistic Model for Point Cloud Compression at Low Bit-Rates
RoPECraft: Training-Free Motion Transfer with Trajectory-Guided RoPE Optimization on Diffusion Transformers
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