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/srikanthmandru/Image-Super-Resolution-SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/srikanthmandru/Super-Resolution-of-Images-Videos-using-SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/ssulun/pytorch-pixelshuffle1d
https://github.com/steveazzolin/SinGan3D
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/sunilbelde/Imagedenoising-dncnn-keras
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/sunilbelde/Imagedenoising-dncnn-ridnet-keras
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/sweetcocoa/IKC
https://github.com/swordgeek/SR
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/szWingLee/spsr-master
Structure-Preserving Super Resolution with Gradient Guidance
https://github.com/t-ae/singan-s4tf
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/takuseno/singan-nnabla
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/teakkkz/imageSR
https://github.com/tegg89/SRCNN-Tensorflow
https://github.com/tensorflow/models
Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection
https://github.com/tensorflow/models/tree/master/research/gan
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/tensorflow/models/tree/master/research/object_detection
Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection
https://github.com/tensorlayer/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/tetrachrome/subpixel
https://github.com/tg-bomze/Face-Depixelizer
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/thatbrguy/Dehaze-GAN
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/the-learning-and-vision-atelier-lava/pam
Parallax Attention for Unsupervised Stereo Correspondence Learning
https://github.com/theotziol/Depth-images-Denoising
https://github.com/thstkdgus35/EDSR-PyTorch
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/tianbaochou/YOUKU-VSRE-2019-49th
https://github.com/tianxingxia-cn/LIIF-Paddle
Learning Continuous Image Representation with Local Implicit Image Function
https://github.com/tiasmondal/SRGAN-keras-google-colab
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/tinnunculus/Denoising_SR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/titu1994/Image-Super-Resolution
https://github.com/titu1994/Super-Resolution-using-Generative-Adversarial-Networks
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/tjjj686/dl_project_srgan
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/tohinz/ConSinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/tom-doerr/TecoGAN
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/tom-doerr/TecoGAN-Docker
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/tomtrac/SRGAN_power_data_generation
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/tony23545/SinGAN_
SinGAN: Learning a Generative Model from a Single Natural Image
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.