https://github.com/hawkiyc/WaveGAN_for_12_Leads_ECG_Signals
A Generative Adversarial Approach To ECG Synthesis And Denoising
Papers with Code 아카이브
Denoising
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/hazdzz/BGU
https://github.com/hejingwenhejingwen/AdaFM
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers
https://github.com/hekstra-lab/phase-retrieval
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/hellloxiaotian/DudeNet
https://github.com/henryaddison/score_sde_pytorch
https://github.com/hershey890/optimization_deep_priors
https://github.com/hi-paris/deepdespeckling
SAR2SAR: a semi-supervised despeckling algorithm for SAR images
https://github.com/hihellohowareyou/RESREF_Chatbot_data_for_Korean
https://github.com/hkunlp/reparam-discrete-diffusion
Structured Denoising Diffusion Models in Discrete State-Spaces
https://github.com/hlzhang109/ddg
Domain Generalization for Object Recognition with Multi-task Autoencoders
https://github.com/hoainamken/Burst-Denoising-of-Dark-Images
https://github.com/hoiliu-0801/dq-detr
A DeNoising FPN With Transformer R-CNN for Tiny Object Detection
https://github.com/hongpeng-guo/deep-image-prior
https://github.com/horenbergerb/BernoulliDiffusion
https://github.com/hrluo/sphericalrotationdimensionreduction
Efficient Manifold and Subspace Approximations with Spherelets
https://github.com/huage001/urae
SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
https://github.com/huangxt39/BART_on_COVID_dialogue
https://github.com/huggingface/transformers
https://github.com/hugolan/Project-Bachelor
https://github.com/hungyiwu/average-joe
https://github.com/hust512/tensor_decoding_spectral_SCI_cameras
Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising
https://github.com/hustvl/PixelHacker
PixelHacker: Image Inpainting with Structural and Semantic Consistency
https://github.com/huyvd7/huyvd7
Deep Graph Laplacian Regularization for Robust Denoising of Real Images
https://github.com/huyvnphan/Learning-To-See-In-The-Dark
https://github.com/hyeongyuy/CT-CYCLE_IDNETITY_GAN_tensorflow
Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography
https://github.com/hyeongyuy/CT-WGAN_VGG_tensorflow
https://github.com/hyunwoongko/asian-bart
Multilingual Denoising Pre-training for Neural Machine Translation
https://github.com/hzlbbfrog/generative-bim
DDIM sampling for Generative AIBIM, a faster intelligent structural design framework
https://github.com/i2r-simmc/i2r-simmc-2020
https://github.com/iPERDance/iPERCore
Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis
https://github.com/ibrahimethemhamamci/dentex
Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays
https://github.com/icthrm/sc-net
https://github.com/idealo/image-super-resolution
https://github.com/ihaeyong/pfnr
https://github.com/ihaeyong/pnr
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.