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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.
Total Variation-Based Image Decomposition and Denoising for Microscopy Images
Learning Cocoercive Conservative Denoisers via Helmholtz Decomposition for Poisson Inverse Problems
You Only Look One Step: Accelerating Backpropagation in Diffusion Sampling with Gradient Shortcuts
DanceGRPO: Unleashing GRPO on Visual Generation
Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach
SparseMeXT Unlocking the Potential of Sparse Representations for HD Map Construction
Topology Guidance: Controlling the Outputs of Generative Models via Vector Field Topology
Technical Report for ICRA 2025 GOOSE 2D Semantic Segmentation Challenge: Leveraging Color Shift Correction, RoPE-Swin Backbone, and Quantile-based Label Denoising Strategy for Robust Outdoor Scene Understanding
2025 TGRS A Self-Supervised Method for Seismic Random Noise Attenuation under Non-Pixelwise Independent Assumption
ProFashion: Prototype-guided Fashion Video Generation with Multiple Reference Images
Burger: Robust Graph Denoising-augmentation Fusion and Multi-semantic Modeling in Social Recommendation
Automated Learning of Semantic Embedding Representations for Diffusion Models
Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions
Towards order of magnitude X-ray dose reduction in breast cancer imaging using phase contrast and deep denoising
Auto Tensor Singular Value Thresholding: A Non-Iterative and Rank-Free Framework for Tensor Denoising
Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review
Graffe: Graph Representation Learning via Diffusion Probabilistic Models
Inter-Diffusion Generation Model of Speakers and Listeners for Effective Communication
MDAA-Diff: CT-Guided Multi-Dose Adaptive Attention Diffusion Model for PET Denoising
Overcoming Dimensional Factorization Limits in Discrete Diffusion Models through Quantum Joint Distribution Learning
Denoising Diffusion Probabilistic Models for Coastal Inundation Forecasting
EDmamba: A Simple yet Effective Event Denoising Method with State Space Model
Flow-GRPO: Training Flow Matching Models via Online RL
DiffusionSfM: Predicting Structure and Motion via Ray Origin and Endpoint Diffusion
Score-based Self-supervised MRI Denoising
Non-stationary Diffusion For Probabilistic Time Series Forecasting
TS-Diff: Two-Stage Diffusion Model for Low-Light RAW Image Enhancement
Riemannian Denoising Diffusion Probabilistic Models
CountDiffusion: Text-to-Image Synthesis with Training-Free Counting-Guidance Diffusion
Spectral and Temporal Denoising for Differentially Private Optimization
Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction
FEMSN: Frequency-Enhanced Multiscale Network for fault diagnosis of rotating machinery under strong noise environments
Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability
Plug-and-Play AMC: Context Is King in Training-Free, Open-Set Modulation with LLMs
Enhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.