Papers with Code 아카이브
Image Denoising
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 21
- 보존된 평가 기록
- 110
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.
Multi-Stage Progressive Image Restoration
Despeckling Sentinel-1 GRD images by deep learning and application to narrow river segmentation
An Interpretation of Regularization by Denoising and its Application with the Back-Projected Fidelity Term
Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising
Blind Image Denoising and Inpainting Using Robust Hadamard Autoencoders
Exploring ensembles and uncertainty minimization in denoising networks
Stochastic Image Denoising by Sampling from the Posterior Distribution
Hyperspectral Image Denoising via Multi-modal and Double-weighted Tensor Nuclear Norm
Image Denoising using Attention-Residual Convolutional Neural Networks
Deep Universal Blind Image Denoising
Context-Aware Image Denoising with Auto-Threshold Canny Edge Detection to Suppress Adversarial Perturbation
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images
Hyperspectral image denoising based on global and non-local low-rank factorizations
Quaternion higher-order singular value decomposition and its applications in color image processing
NBNet: Noise Basis Learning for Image Denoising with Subspace Projection
A Plug-and-Play Priors Framework for Hyperspectral Unmixing
Memory-Efficient Hierarchical Neural Architecture Search for Image Restoration
Towards Boosting the Channel Attention in Real Image Denoising : Sub-band Pyramid Attention
Evolutionary Variational Optimization of Generative Models
Bayesian Image Reconstruction using Deep Generative Models
Color Image Restoration Exploiting Inter-channel Correlation with a 3-stage CNN
Noise2Kernel: Adaptive Self-Supervised Blind Denoising using a Dilated Convolutional Kernel Architecture
SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising
Adaptive noise imitation for image denoising
SAR Image Despeckling Based on Convolutional Denoising Autoencoder
Unsupervised Deep Video Denoising
Lattice Fusion Networks for Image Denoising
Image Denoising for Strong Gaussian Noises With Specialized CNNs for Different Frequency Components
Rank-One Network: An Effective Framework for Image Restoration
Image Denoising by Gaussian Patch Mixture Model and Low Rank Patches
Robust super-resolution depth imaging via a multi-feature fusion deep network
Channel Estimation for Large Intelligent Surface Aided MISO Communications: From LMMSE to Deep Learning Solutions
Noise2Stack: Improving Image Restoration by Learning from Volumetric Data
Suppression of Correlated Noise with Similarity-based Unsupervised Deep Learning
A Comprehensive Comparison of Multi-Dimensional Image Denoising Methods
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.