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Image Super-Resolution
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Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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/Oishani/Machine-Learning-Projects
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/pixelshuffle_invert_pytorch
https://github.com/Ourshanabi/Burst-denoising
https://github.com/PaulEmmanuelSotir/DeepCV
SinGAN: Learning a Generative Model from a Single Natural Image
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/Prachi-Agr/Image-Super-Resolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Pulkitdzrt/ML-Image-Super-Resolution
https://github.com/QLinhHub/Image-super-resolution
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/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/S-aiueo32/SRRAM
MAMNet: Multi-path Adaptive Modulation Network for Image Super-Resolution
https://github.com/S-aiueo32/srntt-pytorch
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.