Papers with Code 아카이브
Grayscale Image Denoising
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/IVRL/BUIFD
공식 코드Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning
https://github.com/cswin/RC-Nets
공식 코드Image Restoration Using Deep Regulated Convolutional Networks
https://github.com/cszn/DnCNN
공식 코드Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/cszn/FFDNet
공식 코드FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
https://github.com/cszn/ircnn
공식 코드https://github.com/diegovalsesia/gcdn
공식 코드https://github.com/grishavak/LIDIA-denoiser
공식 코드LIDIA: Lightweight Learned Image Denoising with Instance Adaptation
https://github.com/jingyunliang/swinir
공식 코드https://github.com/lpj0/MWCNN
공식 코드https://github.com/majedelhelou/BUIFD
공식 코드Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning
https://github.com/megvii-research/TLC
공식 코드Improving Image Restoration by Revisiting Global Information Aggregation
https://github.com/mogvision/adl
공식 코드https://github.com/nikopj/cdlnet-ojsp
공식 코드CDLNet: Robust and Interpretable Denoising Through Deep Convolutional Dictionary Learning
https://github.com/nikopj/groupcdl-tip
공식 코드https://github.com/swz30/restormer
공식 코드Restormer: Efficient Transformer for High-Resolution Image Restoration
https://github.com/visinf/n3net
공식 코드https://github.com/yulunzhang/RDN
공식 코드https://github.com/zhangyi-3/kbnet
공식 코드https://github.com/2023-MindSpore-1/ms-code-211/tree/main/DnCNN
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/IndexNet
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RDN
https://github.com/2023-MindSpore-4/Code-5/tree/main/IndexNet
https://github.com/7568/ffdnet-pytorch
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
https://github.com/AdneneBoumessouer/Anomaly-Detection
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/AdneneBoumessouer/LEGO-Anomaly-Detection
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/AdneneBoumessouer/MVTec-Anomaly-Detection
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/Aoi-hosizora/FFDNet_pytorch
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
https://github.com/Build-Week-Spotify-Song-Suggester-5/Data-Science
https://github.com/GarrickZ2/Image-Denoising
Restormer: Efficient Transformer for High-Resolution Image Restoration
https://github.com/Georg030/MRI-Image-Denoising
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/HDCVLab/MC-Blur-Dataset
Restormer: Efficient Transformer for High-Resolution Image Restoration
https://github.com/IlyaKodua/dncnn
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/LabForComputationalVision/bias_free_denoising
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/LucasElbert/FFDNet
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
https://github.com/MKFMIKU/VIDM
Restormer: Efficient Transformer for High-Resolution Image Restoration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.