Papers with Code 아카이브
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.
Robust Deep Ensemble Method for Real-world Image Denoising
Poisson2Sparse: Self-Supervised Poisson Denoising From a Single Image
Supervised Denoising of Diffusion-Weighted Magnetic Resonance Images Using a Convolutional Neural Network and Transfer Learning
Thunder: Thumbnail based Fast Lightweight Image Denoising Network
A Coupling Enhancement Algorithm for ZrO2 Ceramic Bearing Ball Surface Defect Detection Based on Cartoon-texture Decomposition Model and Multi-Scale Filtering Method
Dense residual Transformer for image denoising
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imaging
Joint Image Compression and Denoising via Latent-Space Scalability
Unsupervised Denoising of Optical Coherence Tomography Images with Dual_Merged CycleWGAN
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset
Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation
PnP-ReG: Learned Regularizing Gradient for Plug-and-Play Gradient Descent
Adversarial Distortion Learning for Medical Image Denoising
A Multi-Head Convolutional Neural Network With Multi-path Attention improves Image Denoising
Gabor is Enough: Interpretable Deep Denoising with a Gabor Synthesis Dictionary Prior
Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach
Shallow camera pipeline for night photography rendering
Learning Enriched Features for Fast Image Restoration and Enhancement
Fast Multi-grid Methods for Minimizing Curvature Energy
HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising Package
SUMD: Super U-shaped Matrix Decomposition Convolutional neural network for Image denoising
Negligible effect of brain MRI data preprocessing for tumor segmentation
Simple Baselines for Image Restoration
Dancing under the stars: video denoising in starlight
Real Image Denoising With a Locally-Adaptive Bitonic Filter
DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors
Total Variation Optimization Layers for Computer Vision
Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training
Zero-shot Blind Image Denoising via Implicit Neural Representations
Estimating Fine-Grained Noise Model via Contrastive Learning
Connections between Deep Equilibrium and Sparse Representation Models with Application to Hyperspectral Image Denoising
Reverse Engineering of Imperceptible Adversarial Image Perturbations
CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image
On the Kullback-Leibler divergence between pairwise isotropic Gaussian-Markov random fields
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