https://github.com/zudi-lin/rcan-it
공식 코드Revisiting RCAN: Improved Training for Image Super-Resolution
Papers with Code 아카이브
Image Super-Resolution
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Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zxlation/FC2N
공식 코드FC$^2$N: Fully Channel-Concatenated Network for Single Image Super-Resolution
https://github.com/04RR/SOTA-Vision
Single Image Super-Resolution via a Holistic Attention Network
https://github.com/07Agarg/Image-Resolution-Enhancement-SRCNN
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/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-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-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/Code10/tree/main/EDSR
Enhanced Deep Residual Networks for Single Image Super-Resolution
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/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
https://github.com/2023-MindSpore-4/Code7/tree/main/srcnn
https://github.com/2024-MindSpore-1/Code4/tree/main/EDSR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/2024-MindSpore-1/Code7/tree/main/DBPN
Deep Back-Projection Networks for Single Image Super-resolution
https://github.com/2024-MindSpore-1/Code7/tree/main/csd
Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
https://github.com/4m4npr33t/SuperResolution-using-GANs
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/545641826/espcn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.