https://github.com/Chad4545/styletransfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Papers with Code 아카이브
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/ChengBinJin/Real-time-style-transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ChenyangLEI/deep-video-prior
Neural Radiance Flow for 4D View Synthesis and Video Processing
https://github.com/CorentinMAG/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/CreativeCodingLab/DeepIllumination
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/DAMI211/srgan
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/DaneyAlex5/Deep-Zooming-of-Images-using-SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Daniahblog/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Davidwang96/my-ZSSR
https://github.com/DerrickXuNu/OpenCOOD
https://github.com/Dictator890/ESRGAN-Android-Demo
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/DivJAth/DeepLearning5922
https://github.com/Divsigma/2020-cs213n
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/DmitryUlyanov/texture_nets
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Dragoniss/mindspore-phase2-Deep-Fourier-Upsampling/blob/main/Fourier-Upsampling.py
https://github.com/DwightFoster/Pytorch-TecoGan
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/Eisbaer8/DeepGTAV
Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?
https://github.com/ErikFaustmann/styletransfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/FaceOnLive/License-Plate-Recognition-SDK-Android
Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution
https://github.com/FeliMe/multimodal_style_transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Feynman1999/MgeEditing
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/FirstPotatoMan/TensorflowJS_ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/Fivefold/SRCNN
https://github.com/FriedRonaldo/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Fuhui-Chen/TecoGAN
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/GaryMataev/DeepRED
https://github.com/GatorSense/SRrootimaging
Accelerating the Super-Resolution Convolutional Neural Network
https://github.com/Gaurav14cs17/Reparameterization-Denoising
Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution
https://github.com/Georg030/MRI-Image-Denoising
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/GinZhu/MIASSR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/GitDT11/EDSR-pytorch
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/GitHubXlong/TecoGAN
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/Gowti-AiboT/GAN-based-SISR-Keras
https://github.com/GuillaumeDufau/Image-super-resolution-vdsr
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
https://github.com/GuoQuanhao/PRNet-Paddle
Progressive Image Deraining Networks: A Better and Simpler Baseline
https://github.com/HERMINDERSINGH1234/ML_Extra_Resolution_Increases
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.