Papers with Code 아카이브
Super-Resolution
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Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.
<span style="color:grey; opacity: 0.6">( Credit: MemNet )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ryanwebster90/gen-overfitting-latent-recovery
Detecting Overfitting of Deep Generative Networks via Latent Recovery
https://github.com/ryanxingql/image-quality-assessment-toolbox
Learning a No-Reference Quality Metric for Single-Image Super-Resolution
https://github.com/ryyAudrey/ConMixSinGAN-master
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/saeed-anwar/DRLN
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/safwankdb/Deep-Image-Prior
https://github.com/safwankdb/Vanilla-GAN
https://github.com/sainathdevulapalli/Super-Resolution-GAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/sainzerjj/sferd
https://github.com/sala-group/autows-bench-101
Fourier Neural Operator for Parametric Partial Differential Equations
https://github.com/samsh19/ML_project
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/sandeeplbscek/awesome-deeplearning-resources
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/saurabh-kataria/projected-distribution-loss
https://github.com/saurabh23kgp/GAN
https://github.com/sayannath/Low-Light-Image-Enhancement
Learning Enriched Features for Real Image Restoration and Enhancement
https://github.com/sayannath/MIRNet-Flutter
Learning Enriched Features for Real Image Restoration and Enhancement
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/sciml/neuraloperators.jl
Fourier Neural Operator for Parametric Partial Differential Equations
https://github.com/sczhou/upscale-a-video
Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video 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/seyedsaleh/music-generator
https://github.com/sgrvinod/a-pytorch-tutorial-to-super-resolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.