https://github.com/yangyucheng000/ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Papers with Code 아카이브
Video Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/yhjo09/VSR-DUF
https://github.com/yjn870/srcnn-pytorch
https://github.com/yslenjoy/machine_learning_SuperResolution
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
https://github.com/zhangtianmingxp/rbpn_mindspore
Recurrent Back-Projection Network for Video Super-Resolution
https://github.com/zhusiling/EDSR
https://github.com/zhusiling/EDVR
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/zhusiling/TecoGAN
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.