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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.
2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning
Normalization-Equivariant Neural Networks with Application to Image Denoising
Fast and Interpretable Nonlocal Neural Networks for Image Denoising via Group-Sparse Convolutional Dictionary Learning
Invisible Image Watermarks Are Provably Removable Using Generative AI
Infrared Image Deturbulence Restoration Using Degradation Parameter-Assisted Wide & Deep Learning
Fast and Accurate Estimation of Low-Rank Matrices from Noisy Measurements via Preconditioned Non-Convex Gradient Descent
Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising
A Dive into SAM Prior in Image Restoration
Realistic Noise Synthesis with Diffusion Models
Restore Anything Pipeline: Segment Anything Meets Image Restoration
NODE-ImgNet: a PDE-informed effective and robust model for image denoising
SS-BSN: Attentive Blind-Spot Network for Self-Supervised Denoising with Nonlocal Self-Similarity
Adaptive aggregation of Monte Carlo augmented decomposed filters for efficient group-equivariant convolutional neural network
CS-PCN: Context-Space Progressive Collaborative Network for Image Denoising
Neural information coding for efficient spike-based image denoising
SwinIA: Self-Supervised Blind-Spot Image Denoising without Convolutions
Real-World Denoising via Diffusion Model
Dual Residual Attention Network for Image Denoising
Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising
A multimodal dynamical variational autoencoder for audiovisual speech representation learning
The Devil is in the Upsampling: Architectural Decisions Made Simpler for Denoising with Deep Image Prior
Cross-domain Denoising for Low-dose Multi-frame Spiral Computed Tomography
H2TF for Hyperspectral Image Denoising: Where Hierarchical Nonlinear Transform Meets Hierarchical Matrix Factorization
Revisiting Implicit Neural Representations in Low-Level Vision
Multi-scale Adaptive Fusion Network for Hyperspectral Image Denoising
Self-supervised Image Denoising with Downsampled Invariance Loss and Conditional Blind-Spot Network
A Comparison of Image Denoising Methods
Unsupervised Image Denoising with Score Function
EWT: Efficient Wavelet-Transformer for Single Image Denoising
DDT: Dual-branch Deformable Transformer for Image Denoising
MCDIP-ADMM: Overcoming Overfitting in DIP-based CT reconstruction
RED-PSM: Regularization by Denoising of Factorized Low Rank Models for Dynamic Imaging
DCANet: Dual Convolutional Neural Network with Attention for Image Blind Denoising
MM-BSN: Self-Supervised Image Denoising for Real-World with Multi-Mask based on Blind-Spot Network
Image Blind Denoising Using Dual Convolutional Neural Network with Skip Connection
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