Papers with Code 아카이브
Video Super-Resolution
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Video Super-Resolution is a computer vision task that aims to increase the resolution of a video sequence, typically from lower to higher resolutions. The goal is to generate high-resolution video frames from low-resolution input, improving the overall quality of the video.
<span style="color:grey; opacity: 0.6">( Image credit: Detail-revealing Deep Video Super-Resolution )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/nannau/DoWnGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/niazwazir/REAL_TIME_VIDEO_IMSR
https://github.com/nihui/realsr-ncnn-vulkan
https://github.com/npielawski/pytorch_tiramisu
https://github.com/nsubbaian/FrequentistML
https://github.com/olaviinha/NeuralImageSuperResolution
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/omglu93/energy_consumption_eda_prediction
https://github.com/omkarghugarkar007/Neural_Super_Sampling
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/open-mmlab/mmediting/blob/master/configs/restorers/basicvsr/README.md
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
https://github.com/open-mmlab/mmsr
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/osofr/xgboost
https://github.com/peteryuX/esrgan-tf2
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/pierobeat/Hoax-News-Classification
https://github.com/pilot7747/sldl
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
https://github.com/pohwa065/Deep-Learning-for-Super-Resolution-and-Image-Enhancement
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/pohwa065/SRGAN-for-Super-Resolution-and-Image-Enhancement
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/poikilos/pyrotocanvas
https://github.com/poyushen/classifaction
https://github.com/psychopa4/PFNL
https://github.com/pwc-1/Paper-9/tree/main/4/pixelshuffle1d
https://github.com/pwc-1/Paper-9/tree/main/7/pixelshuffle1d
https://github.com/quin-med-harvard-edu/kidney
https://github.com/r06922019/butt_lion_paper_notes
https://github.com/rajatthepagal/DBPN-Keras
https://github.com/rami0205/ngramswin
https://github.com/renyang-home/ntire22_venh_sr
BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment
https://github.com/rharish101/DeFMO
https://github.com/rkem1542/EDSR-pytorch
https://github.com/rozumden/ShapeFromBlur
https://github.com/rozumden/tbd
https://github.com/rozumden/tbd3d
https://github.com/ryanhalabi/starcraft_super_resolution
https://github.com/sanghyun-son/EDSR-PyTorch
https://github.com/sberbank-ai/DigiTales
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/sberbank-ai/DigiTeller
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.