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Image Denoising
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Image Denoising is a computer vision task that involves removing noise from an image. Noise can be introduced into an image during acquisition or processing, and can reduce image quality and make it difficult to interpret. Image denoising techniques aim to restore an image to its original quality by reducing or removing the noise, while preserving the important features of the image.
<span style="color:grey; opacity: 0.6">( Image credit: Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Learning Multi-scale Spatial-frequency Features for Image Denoising
A Real-time Endoscopic Image Denoising System
Revisiting Transformers with Insights from Image Filtering
Pseudo-Siamese Blind-Spot Transformers for Self-Supervised Real-World Denoising
YOND: Practical Blind Raw Image Denoising Free from Camera-Specific Data Dependency
A Poisson-Guided Decomposition Network for Extreme Low-Light Image Enhancement
Unrolling Nonconvex Graph Total Variation for Image Denoising
STAR-Net: An Interpretable Model-Aided Network for Remote Sensing Image Denoising
Optimal Density Functions for Weighted Convolution in Learning Models
Optimal Weighted Convolution for Classification and Denosing
Rotation-Equivariant Self-Supervised Method in Image Denoising
Image denoising as a conditional expectation
Degradation-Aware Feature Perturbation for All-in-One Image Restoration
Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problems
Towards order of magnitude X-ray dose reduction in breast cancer imaging using phase contrast and deep denoising
Multi-View Learning with Context-Guided Receptance for Image Denoising
Edge-preserving Image Denoising via Multi-scale Adaptive Statistical Independence Testing
Noise Modeling in One Hour: Minimizing Preparation Efforts for Self-supervised Low-Light RAW Image Denoising
EchoNet-Quality: Denoising Echocardiograms via Deep Generative Modeling of Ultrasound Noise
Self-Supervised Noise Adaptive MRI Denoising via Repetition to Repetition (Rep2Rep) Learning
ECGDeDRDNet: A deep learning-based method for Electrocardiogram noise removal using a double recurrent dense network
Filter2Noise: Interpretable Self-Supervised Single-Image Denoising for Low-Dose CT with Attention-Guided Bilateral Filtering
The Tenth NTIRE 2025 Image Denoising Challenge Report
Efficient and Robust Remote Sensing Image Denoising Using Randomized Approximation of Geodesics' Gramian on the Manifold Underlying the Patch Space
Efficient Medical Image Restoration via Reliability Guided Learning in Frequency Domain
VibrantLeaves: A principled parametric image generator for training deep restoration models
V2V3D: View-to-View Denoised 3D Reconstruction for Light-Field Microscopy
SU-YOLO: Spiking Neural Network for Efficient Underwater Object Detection
DiffDenoise: Self-Supervised Medical Image Denoising with Conditional Diffusion Models
RELD: Regularization by Latent Diffusion Models for Image Restoration
Riemannian Optimization on Relaxed Indicator Matrix Manifold
Dig2DIG: Dig into Diffusion Information Gains for Image Fusion
Fractal-IR: A Unified Framework for Efficient and Scalable Image Restoration
DnLUT: Ultra-Efficient Color Image Denoising via Channel-Aware Lookup Tables
Patch-based learning of adaptive Total Variation parameter maps for blind image denoising
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.