Papers with Code 아카이브
Denoising
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- 평가표
- 6
- 보존된 평가 기록
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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/Namkyeong/NoisyNodes_Pytorch
Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond
https://github.com/NatiOfir/DeepFaintEdges
Multi-scale Processing of Noisy Images using Edge Preservation Losses
https://github.com/Nikolai10/Diffusion-TF
Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration
https://github.com/OdysseasKr/online-nilm
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
https://github.com/OlafChrist-github/Image-Denoising-using-Keras
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/Ourshanabi/Burst-denoising
https://github.com/Owen-Oertell/rlcm
https://github.com/PSCLab-ASU/OpenICS
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/pipelines/examples/text_to_image
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/ppdiffusers
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/PeterBeben/continuous-K-SVD
Cloud Dictionary: Sparse Coding and Modeling for Point Clouds
https://github.com/Pulkitdzrt/ML-Image-Super-Resolution
https://github.com/QLinhHub/Image-super-resolution
https://github.com/Rishit-dagli/MIRNet-TFJS
Learning Enriched Features for Real Image Restoration and Enhancement
https://github.com/Ryosaeba8/DLMI
https://github.com/Ryosaeba8/Medical-Imaging-LOW-DOSE-CT-DENOISING
https://github.com/S-Abdelnabi/awt
Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding
https://github.com/SJTU-Det/R3Det_Tensorflow
https://github.com/SSinyu/WGAN-VGG
https://github.com/SamirMitha/Denoising
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
https://github.com/Scientific-Research-Algorithm-Toolbox/SCI-algorithms
Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging
https://github.com/SergiBragos/DLAI-2021-project
Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network
https://github.com/ShakedDovrat/JpegArtifactRemoval
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/Shakib-IO/Diminishing_Image_Noise_Using_Deep_Learning
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/ShaofengZou/A-CNN-Based-Blind-Denoising-Method
https://github.com/Sharda-Borse/AutoEncoders
https://github.com/SinclairHudson/RePaint
https://github.com/SkyworkAI/SkyReels-A1
SkyReels-Audio: Omni Audio-Conditioned Talking Portraits in Video Diffusion Transformers
https://github.com/Slimnios/SaGess
SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation
https://github.com/SnailDev/github-hot-hub
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/SparkZu/RadioLLM
https://github.com/SusungHong/IF-DreamFusion
https://github.com/TAMU-VITA/USAID
Segmentation-Aware Image Denoising without Knowing True Segmentation
https://github.com/Tae-Eon/Gen-CUDE
Unsupervised Neural Universal Denoiser for Finite-Input General-Output Noisy Channel
https://github.com/TanviKulkarni07/CS-MRI-Recon
Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.