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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.
Assessing the Impact of Deep Neural Network-based Image Denoising on Binary Signal Detection Tasks
Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images
Frequency Domain Loss Function for Deep Exposure Correction of Dark Images
Invertible Denoising Network: A Light Solution for Real Noise Removal
Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detection
Hyperspectral Image Denoising Based On Multi-Stream Denoising Network
Self-Supervised Learning based CT Denoising using Pseudo-CT Image Pairs
Searching Efficient Model-guided Deep Network for Image Denoising
Interpretable Unsupervised Diversity Denoising and Artefact Removal
Variational Deep Image Denoising
Deep Contrastive Patch-Based Subspace Learning for Camera Image Signal Processing
Modeling Graph Node Correlations with Neighbor Mixture Models
Task-Oriented Low-Dose CT Image Denoising
Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition
New Computational Techniques for a Faster Variation of BM3D Image Denoising
Data Discovery Using Lossless Compression-Based Sparse Representation
Hyperspectral Image Denoising and Anomaly Detection Based on Low-rank and Sparse Representations
An unsupervised deep learning framework for medical image denoising
Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations
COLA-Net: Collaborative Attention Network for Image Restoration
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie
Convolutional Neural Network Denoising in Fluorescence Lifetime Imaging Microscopy (FLIM)
High Perceptual Quality Image Denoising with a Posterior Sampling CGAN
CDLNet: Robust and Interpretable Denoising Through Deep Convolutional Dictionary Learning
BM3D vs 2-Layer ONN
Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image Denoising
Feature-Align Network with Knowledge Distillation for Efficient Denoising
Efficient Deep Image Denoising via Class Specific Convolution
Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting
Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-Case
Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priors
Uncertainty-aware Generalized Adaptive CycleGAN
EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks
ISCL: Interdependent Self-Cooperative Learning for Unpaired Image Denoising
NFCNN: Toward a Noise Fusion Convolutional Neural Network for Image Denoising
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