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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.
MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion
Adversarial Defense via Image Denoising with Chaotic Encryption
Time-series image denoising of pressure-sensitive paint data by projected multivariate singular spectrum analysis
A Noise-level-aware Framework for PET Image Denoising
Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spots
Multi-Scale Adaptive Network for Single Image Denoising
Learning to Bound: A Generative Cramér-Rao Bound
Adaptive Cross-Layer Attention for Image Restoration
Selective Residual M-Net for Real Image Denoising
Towards a unified view of unsupervised non-local methods for image denoising: the NL-Ridge approach
SUNet: Swin Transformer UNet for Image Denoising
C2N: Practical Generative Noise Modeling for Real-World Denoising
Ab-initio Contrast Estimation and Denoising of Cryo-EM Images
Reducing Redundancy in the Bottleneck Representation of the Autoencoders
Posterior temperature optimized Bayesian models for inverse problems in medical imaging
Content-Noise Complementary Learning for Medical Image Denoising
Practical Noise Simulation for RGB Images
Improving the quality control of seismic data through active learning
On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent
Towards Adversarially Robust Deep Image Denoising
Model-Based Image Signal Processors via Learnable Dictionaries
MAXIM: Multi-Axis MLP for Image Processing
Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization
Fast and High-Quality Image Denoising via Malleable Convolutions
Image Denoising with Control over Deep Network Hallucination
Image Edge Restoring Filter
Ultrasound Speckle Suppression and Denoising using MRI-derived Normalizing Flow Priors
Exploring Inter-frequency Guidance of Image for Lightweight Gaussian Denoising
A Novel Image Denoising Algorithm Using Concepts of Quantum Many-Body Theory
EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation
Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow
Improving Image Restoration by Revisiting Global Information Aggregation
A comparison study of CNN denoisers on PRNU extraction
Noise Distribution Adaptive Self-Supervised Image Denoising using Tweedie Distribution and Score Matching
SAR Image Despeckling Using Continuous Attention Module
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