https://github.com/zhusiling/RCAN
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
Papers with Code 아카이브
3D Object Super-Resolution
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3D object super-resolution is the task of up-sampling 3D objects.
<span style="color:grey; opacity: 0.6">( Image credit: Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zhusiling/TecoGAN
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/zhy1001/Fast_Style_Transfer_using_CNN
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/zijundeng/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/ziyangwang007/mamba-unet
https://github.com/zlf1993/SrGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/zsdonghao/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/zsyOAOA/VDNet
https://github.com/zvict/papr
Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation
https://github.com/zxr931120/-
Deep Residual Network for Joint Demosaicing and Super-Resolution
https://gitlab.com/birder/birder
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
https://huggingface.co/isp-uv-es/superIX
Swin2-MoSE: A New Single Image Super-Resolution Model for Remote Sensing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.