Papers with Code 아카이브
Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/VisDrone/DroneVehicle
Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning
https://github.com/WANG-Chaoyue/EvolutionaryGAN-pytorch
https://github.com/WGLab/SGAN
https://github.com/WTCN-computational-anatomy-group/MTVprocess3D
https://github.com/WalterJohnson0/tf-keras-implementation-of-Image-Style-transformation-network
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/WarClans612/deep_learning_final
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/WarrenGreen/srcnn
https://github.com/WeiHan3/dsrn
https://github.com/Weifeng73/Zero-Shot-Super-resolution
https://github.com/WenlongZhang0724/RankSRGAN
RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution
https://github.com/Wenyuan-Vincent-Li/sinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/XHChen0528/SNGAN_Projection_Pytorch
https://github.com/XPixelGroup/BasicSR
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
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/YapengTian/TDAN-VSR-CVPR-2020
TDAN: Temporally Deformable Alignment Network for Video Super-Resolution
https://github.com/YapengTian/TDAN_VSR
TDAN: Temporally Deformable Alignment Network for Video Super-Resolution
https://github.com/YeongHyeon/Super-Resolution_CNN
https://github.com/YeongHyeon/Super-Resolution_CNN-PyTorch
https://github.com/YilinLiu97/Faster-DIP-Recon
https://github.com/Yiming-Miao/Miniproject2
https://github.com/Yuki0114/UndergraduateThesis_SRCNN_keras
https://github.com/YvanG/VQGAN-CLIP
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/ZJULearning/diverse_image_synthesis
https://github.com/ZMpursue/Anime_avatar_generation
https://github.com/ZMpursue/dcgan_fake_face
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/ZhaoHengrun/EDVR
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/ZoieMo/Multi-task
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
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/a-paranjape/mlfundas
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.