https://github.com/code-implementation1/Code6/tree/main/RCAN
Revisiting RCAN: Improved Training for Image Super-Resolution
Papers with Code 아카이브
3D Object Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 0
- 보존된 평가 기록
- 0
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/code-implementation1/Code6/tree/main/ras
Recurrent Back-Projection Network for Video Super-Resolution
https://github.com/code-implementation1/Code7/tree/main/RDN
https://github.com/code-implementation1/Code9/tree/main/EDSR
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
https://github.com/cognitivemodeling/finn
Fourier Neural Operator for Parametric Partial Differential Equations
https://github.com/coloquinte/torchsr
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/colorjam/PAMS
PAMS: Quantized Super-Resolution via Parameterized Max Scale
https://github.com/compvis/fm-boosting
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/compvis/stable-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/cosmostat/wf-psf
https://github.com/cryu854/FastStyle
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/cryu854/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/cs-giung/FSRNet-pytorch
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
https://github.com/cscss/DBVSR
https://github.com/cszhilu1998/selfhdr
Exposure Bracketing Is All You Need For A High-Quality Image
https://github.com/cviaai/PSF-GAN-ESTIMATION
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/cxzhou95/XLSR
Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices
https://github.com/dacostaHugo/AC-GANs
https://github.com/daheyinyin/RCAN
Revisiting RCAN: Improved Training for Image Super-Resolution
https://github.com/daitao/SAN
https://github.com/dalexanderch/CFD
https://github.com/danielenricocahall/Keras-UNet
https://github.com/danilka-na/DegreeWork
https://github.com/darren622672/IQA
https://github.com/dattv/DCSCN-Tensorflow
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network
https://github.com/davidhalladay/CE-compressive-sensing-on-generative-model-pytorch
https://github.com/daymos/simple_keras_GAN
https://github.com/ddehueck/pytorch-GAN
https://github.com/deathstar1/gan
https://github.com/deepak112/Keras-SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/deephdc/oc-search-engine
Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network
https://github.com/deephdc/satsr
Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network
https://github.com/deeplearningnapratica/pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/deforum/stable-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/dgdelahera/tf-mobile-dcscn
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network
https://github.com/dhanush-bhargav/image-superres
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.