https://github.com/Wenyuan-Vincent-Li/sinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
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/XPixelGroup/BasicSR
https://github.com/XiuyuLiu/Application-of-Deep-Learning-models-in-Single-Image-Super-Resolution
https://github.com/XuecaiHu/Meta-SR-Pytorch
Meta-SR: A Magnification-Arbitrary Network for Super-Resolution
https://github.com/YanyingZH/srgan-esrgan-pytorch
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/YeongHyeon/Super-Resolution_CNN
https://github.com/YeongHyeon/Super-Resolution_CNN-PyTorch
https://github.com/Yuki0114/UndergraduateThesis_SRCNN_keras
https://github.com/YvanG/VQGAN-CLIP
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/Zdafeng/SwinFIR
https://github.com/Zhangyanbo/iResNetLab
https://github.com/ZhaoHengrun/EDSR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/Zorrat/Image-Denoiser-Supersampling-and-Editor
Accelerating the Super-Resolution Convolutional Neural Network
https://github.com/ZyoungXu/MoSt-DSA
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/aba450/Super-Resolution
https://github.com/abbasi-ali/noise2self
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/abdulwaheedsoudagar/SR-CNN
https://github.com/abhiskk/fast-neural-style
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/achrafdjerida/Alsat-2B
A new public Alsat-2B dataset for single-image super-resolution
https://github.com/adbobes/VideoSuperResolution
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/advaza/enhancenet_pretrained
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
https://github.com/aijialin/style_transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ajinkya933/Image_repo
https://github.com/akanametov/Pix2Pix
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/akanametov/SuperResolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/akashpalrecha/deblurring-pipeline
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/akashpalrecha/superres-deformable
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/akshaysubr/TEGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/alessandrodicosola/SuperSampling
ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-Resolution
https://github.com/alexjc/neural-enhance
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/algolzw/dcls
Deep Constrained Least Squares for Blind Image Super-Resolution
https://github.com/alililia/ascend_ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/alililia/wdsr_Ascend
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/alililia/wdsr_GPU
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/allenai/satlas-super-resolution
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/alterzero/DBPN-Pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.