https://github.com/OscarAlvaradoM/Operador-Neuronal-de-Fourier
Fourier Neural Operator for Parametric Partial Differential Equations
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/Ourshanabi/Burst-denoising
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/ppdiffusers
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/PaddlePaddle/PaddleScience
Fourier Neural Operator for Parametric Partial Differential Equations
https://github.com/PaddlePaddle/PaddleScience/tree/develop/examples/tempoGAN
tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow
https://github.com/PaulEmmanuelSotir/DeepCV
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Pea-Shooter/ADNet
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/PetruninAlex/Sketch2Real
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Player514/BachelorThesis
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Pol22/NAS_DIP
https://github.com/Prachi-Agr/Image-Super-Resolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/PranjalSahu/InteractiveSmoothingDBT
A Cone-Beam X-Ray CT Data Collection designed for Machine Learning
https://github.com/PrashanthaTP/wav2mov
https://github.com/Pulkitdzrt/ML-Image-Super-Resolution
https://github.com/QLinhHub/Image-super-resolution
https://github.com/Qsingle/imed_vision
Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module
https://github.com/RAF96/ifmo-2019-deep-learning-coursework
https://github.com/REN-HT/A2N
https://github.com/RaDelft/RaDelft-Dataset
Automatic Labelling & Semantic Segmentation with 4D Radar Tensors
https://github.com/Rajas1211/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Rajjeshwar/DCGAN
https://github.com/Ram81/AC-VAEGAN-PyTorch
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
https://github.com/Rayyan-Akhtar/XLSR
Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices
https://github.com/Razi92/GAN_SISR_Pytorch
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/RezisEwig/esrgan
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/RikoLi/gan-acgface
https://github.com/Rishit-dagli/MIRNet-TFJS
Learning Enriched Features for Real Image Restoration and Enhancement
https://github.com/RomanovIgnat/KernelGAN
Blind Super-Resolution Kernel Estimation using an Internal-GAN
https://github.com/S-aiueo32/SRRAM
MAMNet: Multi-path Adaptive Modulation Network for Image Super-Resolution
https://github.com/S-aiueo32/srntt-pytorch
https://github.com/SACHIN92012/Implementation-of-GAN-in-distributed-networks
https://github.com/SDBurt/SRGAN-PT
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/SJHNJU/VDSR
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
https://github.com/SJHNJU/WDSR
Wide Activation for Efficient and Accurate Image Super-Resolution
https://github.com/SJHNJU/wdsr_b
Wide Activation for Efficient and Accurate Image Super-Resolution
https://github.com/Saafke/EDSR_Tensorflow
Enhanced Deep Residual Networks for Single Image Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.