https://github.com/sainathdevulapalli/Super-Resolution-GAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
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/samsh19/ML_project
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/sanghyun-son/EDSR-PyTorch
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/sanglee325/cutblur
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
https://github.com/sangyun884/CinCGAN-pytorch
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks
https://github.com/sanju-27/Video-Super-Resolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/sberbank-ai/DigiTales
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/sberbank-ai/DigiTeller
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/sberbank-ai/real-esrgan
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/sci-sjj/EDSRmodelling
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/sdauzcm/sr-basicsr
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/sdkchris/Exercise-Sheet-1
Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection
https://github.com/sdxone/resolution
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/seanmullery/Super-Resolution
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/seloufian/Faster-Style-Transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/seobeomjin/ML_Project_MRI_Brain_Image_SR
https://github.com/septmars/DL
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/setsunil/dsdnet
https://github.com/seunghwan1228/srgran
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/sgrvinod/a-pytorch-tutorial-to-super-resolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/shangqigao/RONet
Rank-One Network: An Effective Framework for Image Restoration
https://github.com/shangqigao/bayesr
Bayesian Image Super-Resolution with Deep Modeling of Image Statistics
https://github.com/shaonianruntu/MWCNN
https://github.com/shesay-noway/Super-Resolution-RCAN
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/shibuiwilliam/DeepLearningDenoise
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/shimo8810/NTIRE2017
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/shirsenduhalder/SRResGAN-Improved-Perceptual
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/shivsundram/superresolution
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/shnhrtkyk/satellite-super-resolution
The 2018 PIRM Challenge on Perceptual Image Super-resolution
https://github.com/shreeyashyende/better_img_res_with_SRCNN
https://github.com/siavashbigdeli/DMSP
https://github.com/simonsLiang/PReNet_paddle
Progressive Image Deraining Networks: A Better and Simpler Baseline
https://github.com/singnet/super-resolution-service
A Fully Progressive Approach to Single-Image Super-Resolution
https://github.com/skchen1993/SwinIR
https://github.com/sldyns/DnCNN_paddle
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/smc-x/ms-irn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.