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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.
Knowledge Distillation for Speech Denoising by Latent Representation Alignment with Cosine Distance
MRI motion correction via efficient residual-guided denoising diffusion probabilistic models
Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T Tracking
Coop-WD: Cooperative Perception with Weighting and Denoising for Robust V2V Communication
FlexiAct: Towards Flexible Action Control in Heterogeneous Scenarios
Diffusion Models are Secretly Exchangeable: Parallelizing DDPMs via Autospeculation
Multi-View Learning with Context-Guided Receptance for Image Denoising
Dual Prompting for Diverse Count-level PET Denoising
DualReal: Adaptive Joint Training for Lossless Identity-Motion Fusion in Video Customization
Quantizing Diffusion Models from a Sampling-Aware Perspective
Enhancing Black-Litterman Portfolio via Hybrid Forecasting Model Combining Multivariate Decomposition and Noise Reduction
Edge-preserving Image Denoising via Multi-scale Adaptive Statistical Independence Testing
Quaternion Wavelet-Conditioned Diffusion Models for Image Super-Resolution
Safety-Critical Traffic Simulation with Guided Latent Diffusion Model
AI-Driven Segmentation and Analysis of Microbial Cells
A Time-Series Data Augmentation Model through Diffusion and Transformer Integration
Generative diffusion model surrogates for mechanistic agent-based biological models
GarmentDiffusion: 3D Garment Sewing Pattern Generation with Multimodal Diffusion Transformers
MagicPortrait: Temporally Consistent Face Reenactment with 3D Geometric Guidance
Noise Modeling in One Hour: Minimizing Preparation Efforts for Self-supervised Low-Light RAW Image Denoising
PixelHacker: Image Inpainting with Structural and Semantic Consistency
DiffusionRIR: Room Impulse Response Interpolation using Diffusion Models
Adept: Annotation-Denoising Auxiliary Tasks with Discrete Cosine Transform Map and Keypoint for Human-Centric Pretraining
EchoNet-Quality: Denoising Echocardiograms via Deep Generative Modeling of Ultrasound Noise
Generative AI for Physical-Layer Authentication
Adaptive Weight Modified Riesz Mean Filter For High-Density Salt and Pepper Noise Removal
Outlier-aware Tensor Robust Principal Component Analysis with Self-guided Data Augmentation
HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models
Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional
CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors
A Machine Learning Approach for Denoising and Upsampling HRTFs
Self-Supervised Noise Adaptive MRI Denoising via Repetition to Repetition (Rep2Rep) Learning
Evolution Meets Diffusion: Efficient Neural Architecture Generation
Fast Autoregressive Models for Continuous Latent Generation
Diffusion Probabilistic Models for Compressive SAR Imaging
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.