https://github.com/Francis-Rings/MotionEditor
High-Resolution Image Synthesis with Latent Diffusion Models
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/Francis-Rings/MotionFollower
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/FrankwaP/pytorch-toy-GAN
https://github.com/FriedRonaldo/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Fuhui-Chen/TecoGAN
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/G-U-N/Awesome-Consistency-Models
https://github.com/G-U-N/Phased-Consistency-Model
https://github.com/G-U-N/Stable-Consistency-Tuning
https://github.com/GaryMataev/DeepRED
https://github.com/GatorSense/SRrootimaging
Accelerating the Super-Resolution Convolutional Neural Network
https://github.com/Gaurav14cs17/Reparameterization-Denoising
Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution
https://github.com/GauriJagatap/invimaging-deeppriors
Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors
https://github.com/Georg030/MRI-Image-Denoising
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/Gideon0805/Tensorflow1.15-Model-Pruning
Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal
https://github.com/Gideon0805/Tensorflow_Model_Pruning
Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal
https://github.com/GinZhu/MIASSR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/GitDT11/EDSR-pytorch
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/GitHubXlong/TecoGAN
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/Gowti-AiboT/GAN-based-SISR-Keras
https://github.com/GuillaumeDufau/Image-super-resolution-vdsr
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
https://github.com/GuoQuanhao/PRNet-Paddle
Progressive Image Deraining Networks: A Better and Simpler Baseline
https://github.com/HERMINDERSINGH1234/ML_Extra_Resolution_Increases
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/HUuxiaobin/Face-Super-Resolution-Guided-by-3D-Facial-Priors
https://github.com/Hadrien-Cornier/E6040-super-resolution-project
https://github.com/HaijunMa/GAN
https://github.com/HaijunMa/GAN-Getting-started-learning
https://github.com/Harrypotterrrr/EDVR
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/HarukiYqM/pytorch-ZSSR
https://github.com/Hayashi-Yudai/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Hedawl/PINO-MBD
Physics-Informed Neural Operator for Learning Partial Differential Equations
https://github.com/HedgehogCode/deep-plug-and-play-prior
https://github.com/HedgehogCode/denoising-gan
https://github.com/Hem7513/Decision-Trees-and-XGBoost-Algorithm-Documentation
https://github.com/HighVoltageRocknRoll/sr
https://github.com/HilaManor/Generative-deep-features
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/HritwikEkade/Image-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.