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/danielenricocahall/Keras-UNet
https://github.com/danilka-na/DegreeWork
https://github.com/darren622672/IQA
https://github.com/dattv/DCSCN-Tensorflow
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network
https://github.com/deepak112/Keras-SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/deeplearningnapratica/pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/dgdelahera/tf-mobile-dcscn
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network
https://github.com/dhanush-bhargav/image-superres
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/diff7/DARTS-devices
https://github.com/dimawork1981/super_resolution_bot
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/diningeachox/Image-Super-Resolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/divyam96/EESRGAN
https://github.com/djsinghnegi/SRINImageEnhancemrntGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/dkoleber/multi_style_transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/dmcv-ecnu/MindSpore_ModelZoo/tree/main/CSD
Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
https://github.com/dmcv-ecnu/MindSpore_ModelZoo/tree/main/SinGanMindspore
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/dminzi98/super-resolution-for-MRIs
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/dnwjddl/Inpainting
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/doantienthongbku/ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/doantienthongbku/SRGAN-Pytorch
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/dokyeongK/Pytorch_SRCNN
https://github.com/dokyeongK/Single-Image-Super-Resolution-Reimplemenation
https://github.com/dongheehand/RCAN-tf
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/dongyan007/Pretrained-IPT-main-master
https://github.com/dslisleedh/NAFNet-flax
https://github.com/dslisleedh/NCNet-flax
Fast Nearest Convolution for Real-Time Efficient Image Super-Resolution
https://github.com/dxyang/styletransfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ecnusr/etds
https://github.com/edzq/WaveletSRGAN
Image Super-Resolution Using a Wavelet-based Generative Adversarial Network
https://github.com/eltechno/pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/epochlab/xres
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/eriklindernoren/Keras-GAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/eriklindernoren/PyTorch-GAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/etttttte/mayfest2018
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/everything2x/realsr-ncnn-vulkan-python
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.