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/MindSpore-scientific/code-13/tree/main/pixelshuffle1d
PixelShuffler: A Simple Image Translation Through Pixel Rearrangement
https://github.com/MindSpore-scientific/code-4/tree/main/pixelshuffle1d
https://github.com/MindSpore-scientific/code-5/tree/main/Projected-Distribution
https://github.com/MindSpore-scientific/code-8/tree/main/SING
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/MindSpore-scientific/code-8/tree/main/Super-Resolution-for-Root-Imaging
https://github.com/MohamedAliRashad/Crystal
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/MohammedAlkhrashi/4D-Dream
Accelerating the Super-Resolution Convolutional Neural Network
https://github.com/MohammedAlkhrashi/TMA
Accelerating the Super-Resolution Convolutional Neural Network
https://github.com/Mohitkr95/Image-Super-Resolution
https://github.com/Mosasaur5526/BCM-iCT-torch
https://github.com/MrCat9/Sklearn_Note
https://github.com/NJU-PCALab/AddSR
AddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion Distillation
https://github.com/NariMo91/GANs-generative-art
https://github.com/Natsu6767/DCGAN-PyTorch
https://github.com/Ne-ki-tos/Super_res
https://github.com/Neronjust2017/BayesianCNN
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
https://github.com/Nhat-Thanh/ESPCN-Pytorch
https://github.com/Nhat-Thanh/ESPCN-TF
https://github.com/Nhat-Thanh/FSRCNN-Pytorch
Accelerating the Super-Resolution Convolutional Neural Network
https://github.com/Nhat-Thanh/FSRCNN-TF
Accelerating the Super-Resolution Convolutional Neural Network
https://github.com/Nhat-Thanh/PixelRL-SR
https://github.com/Nhat-Thanh/SRCNN-Pytorch
https://github.com/Nhat-Thanh/SRCNN-TF
https://github.com/Nhat-Thanh/VDSR-Pytorch
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
https://github.com/NicicDamjan/SoftKompjuting
https://github.com/NicoCeresa/FSRCNN-2016
Accelerating the Super-Resolution Convolutional Neural Network
https://github.com/NikolaZubic/2dimageto3dmodel
Learning Structral coherence Via Generative Adversarial Network for Single Image Super-Resolution
https://github.com/Nikolai10/Diffusion-TF
Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration
https://github.com/Nirav-Agarwal/GANs
https://github.com/Oishani/Machine-Learning-Projects
https://github.com/OkanKY/generative_adversarial_networks
https://github.com/OlafChrist-github/Image-Denoising-using-Keras
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/OlgaChernytska/Super-Resolution-with-FSRCNN
Accelerating the Super-Resolution Convolutional Neural Network
https://github.com/One-sixth/CVAE-GAN_tensorlayer
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
https://github.com/One-sixth/pixelshuffle_invert_pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.