https://github.com/jpatts/srgan_tf
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Papers with Code 아카이브
Super-Resolution
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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/jpowie01/DCGAN_CelebA
https://github.com/jshermeyer/RFSR
The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery
https://github.com/jshermeyer/VDSR4Geo
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
https://github.com/jskDr/keraspp
https://github.com/jskDr/keraspp_2021
https://github.com/jsll/pytorch_6dof-graspnet
Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation
https://github.com/jtiger958/pytorch-computer-vision-basic
https://github.com/jtiger958/pytorch-computer-vision-tutorial
https://github.com/junhocho/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/junhsss/consistency-models
https://github.com/junjun-jiang/RefESR
https://github.com/junyanz/iGAN
https://github.com/jupiterman/Super-Resolution-Images
https://github.com/kadirnar/bsrgan-pip
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
https://github.com/kai422/iart
Enhancing Video Super-Resolution via Implicit Resampling-based Alignment
https://github.com/kailigo/pacl
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/kainoj/keep-pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/kangyeolk/pytorch-gan-collections
https://github.com/katieluo88/280finalproj_nyudepth
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
https://github.com/kdhRick2222/Exposure-slot
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems
https://github.com/keerthan2/RDN-Tensorflow
https://github.com/keerthan2/Residual-Channel-Attention-Network
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/kekke-gk/OpenAbs
https://github.com/keras-team/keras-cv
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/kfallah/NODE-Denoiser
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/khush3/GAN
https://github.com/khyeyoon/Video-Super-Resolution
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/kiakh93/C-GAN
https://github.com/kidswithtokens/medsegdiff
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model
https://github.com/kingcheng2000/GAN
https://github.com/kingcheng2000/Multi-View-Silhouette-and-Depth-Decomposition-for-High-Resolution-3D-Object-Representation
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation
https://github.com/kingcong/FDSR
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline
https://github.com/kingcong/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/kingcong/csd
Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
https://github.com/kingcong/gpu_ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.