Papers with Code 아카이브
Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.
<span style="color:grey; opacity: 0.6">( Credit: MemNet )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/123YanZou/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/15517965908/keras-DBPN
https://github.com/1hb6s7t/Super-Resolution-for-Root-Imaging-mindspore
https://github.com/20151001860/Real_ESRGAN_paddle
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
https://github.com/2023-MindSpore-1/ms-code-17/tree/main/CGAN
https://github.com/2023-MindSpore-1/ms-code-210/tree/main/CSNL
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/2023-MindSpore-1/ms-code-210/tree/main/DBPN
https://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-213/tree/main/ESRGAN
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HiFaceGAN
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/IPT
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RCAN
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RDN
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/rbpn
Recurrent Back-Projection Network for Video Super-Resolution
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/STGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/sr_ea
Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/srcnn
Enhancement of Anime Imaging Enlargement using Modified Super-Resolution CNN
https://github.com/2023-MindSpore-1/ms-code-222/tree/main/SRFlow
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/2023-MindSpore-1/ms-code-41
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/wdsr
Wide Activation for Efficient and Accurate Image Super-Resolution
https://github.com/2023-MindSpore-4/Code-5/tree/main/GhostSR
GhostSR: Learning Ghost Features for Efficient Image Super-Resolution
https://github.com/2023-MindSpore-4/Code-5/tree/main/HiFaceGAN
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
https://github.com/2023-MindSpore-4/Code-5/tree/main/IRN
https://github.com/2023-MindSpore-4/Code1/tree/main/EDSR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/2023-MindSpore-4/Code1/tree/main/Efficientnet
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
https://github.com/2023-MindSpore-4/Code10/tree/main/EDSR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/2023-MindSpore-4/Code10/tree/main/Efficientnet
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
https://github.com/2023-MindSpore-4/Code10/tree/main/GhostSR
GhostSR: Learning Ghost Features for Efficient Image Super-Resolution
https://github.com/2023-MindSpore-4/Code11/tree/main/SRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/2023-MindSpore-4/Code11/tree/main/sr_ea
Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
https://github.com/2023-MindSpore-4/Code14/tree/main/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/2023-MindSpore-4/Code2/tree/main/DBPN
Deep Back-Projection Networks for Single Image Super-resolution
https://github.com/2023-MindSpore-4/Code6/tree/main/RCAN
Revisiting RCAN: Improved Training for Image Super-Resolution
https://github.com/2023-MindSpore-4/Code6/tree/main/RDN
https://github.com/2023-MindSpore-4/Code7/tree/main/SRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.