Papers with Code 아카이브
Denoising
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 6
- 보존된 평가 기록
- 15
Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.
<span style="color:grey; opacity: 0.6">( Image credit: Beyond a Gaussian Denoiser )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/michaelkoelle/quantum-diffusion
https://github.com/michimoeller/liftingLayers
https://github.com/microsoft/CyBERTron-LM
DiffusionNER: Boundary Diffusion for Named Entity Recognition
https://github.com/microsoft/TUTA_table_understanding
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks
https://github.com/microsoft/Table-Pretraining
https://github.com/microsoft/evodiff
Structured Denoising Diffusion Models in Discrete State-Spaces
https://github.com/microsoft/fastseq
https://github.com/microsoft/hitab
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks
https://github.com/microsoft/mofdiff
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design
https://github.com/microsoft/unispeech
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
https://github.com/microsoft/visual-chatgpt
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/milmor/diffusion-transformer-keras
https://github.com/milmor/latent-diffusion-transformer
https://github.com/mindslab-ai/wavegrad2
https://github.com/mindspore-ai/models/blob/master/research/cv/LearningToSeeInTheDark
https://github.com/mindspore-ai/models/tree/master/official/cv/RDN
https://github.com/mindspore-ai/models/tree/master/official/cv/dncnn
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/mindspore-ai/models/tree/master/official/cv/unet
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images
https://github.com/mindspore-ai/models/tree/master/research/cv/DnCNN
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/mindspore-ai/models/tree/master/research/cv/IPT
https://github.com/mindspore-lab/mindone
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/mingcv/ytmt-strategy
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/mingyue-cheng/crosstimenet
Diffusion Auto-regressive Transformer for Effective Self-supervised Time Series Forecasting
https://github.com/mingyue-cheng/timemae
Diffusion Auto-regressive Transformer for Effective Self-supervised Time Series Forecasting
https://github.com/miriskalt/AnomalyDetectionAutoencoder
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/mirthai/med3dvlm
Geodesic Diffusion Models for Medical Image-to-Image Generation
https://github.com/mizutanilab/schizo-nn
https://github.com/mkachuee/DynamicFeatureAcquisition
https://github.com/mkshing/prompt-plus-pytorch
P+: Extended Textual Conditioning in Text-to-Image Generation
https://github.com/ml-postech/robust-evaluation-of-diffusion-based-purification
https://github.com/ml-research/i2p
https://github.com/mmukhopadhyay/-Efficient-Manifold-Learning-Using-Spherelets
Efficient Manifold and Subspace Approximations with Spherelets
https://github.com/mohammadiha/bnmf
Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization
https://github.com/mohizahmad/DeepDOA-master
https://github.com/motus/DNS-Challenge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.