https://github.com/giannisdaras/sgilo
Intermediate Layer Optimization for Inverse Problems using Deep Generative 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/giovannicatalani/enf2enf_pytorch
Geometry aware inference of steady state PDEs using Equivariant Neural Fields representations
https://github.com/gitaar9/MLDAGAN
https://github.com/glouppe/info8010-deep-learning
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/gmum/LoCondA
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
https://github.com/godpgf/scarn
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network
https://github.com/goldhuang/SRGAN-PyTorch
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/gongenhao/GANCS
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/gongzix/neuroclips
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
https://github.com/google-research/electra
https://github.com/google/RED
The Little Engine that Could: Regularization by Denoising (RED)
https://github.com/gordicaleksa/pytorch-gans
https://github.com/gordicaleksa/pytorch-neural-style-transfer-fast
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/gordicaleksa/pytorch-nst-feedforward
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/gordinirojo/deinterlace
https://github.com/goutamgmb/NTIRE21_BURSTSR
https://github.com/goutamgmb/deep-burst-sr
https://github.com/goutamgmb/deep-rep
https://github.com/gplast/DroNet
DroNet: Efficient convolutional neural network detector for real-time UAV applications
https://github.com/gplast/DroNet_PyTorch
DroNet: Efficient convolutional neural network detector for real-time UAV applications
https://github.com/gq-sonofgod/zoom-picture
https://github.com/gregbugaj/unet-denoiser
Binary Document Image Super Resolution for Improved Readability and OCR Performance
https://github.com/gs18113/ESPCN-TensorFlow2
https://github.com/guillaumeboniface/super_resolution
https://github.com/guillermocarbajal/j-mkpd
https://github.com/guochengqian/PU-GCN
https://github.com/guoyongcs/ews
Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
https://github.com/habout632/gans
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/halaneji/SDT-DCSCN
SDT-DCSCN for Simultaneous Super-Resolution and Deblurring of Text Images
https://github.com/hannahaih/Project-GANDALF
https://github.com/hans/adversarial
https://github.com/haoheliu/versatile_audio_super_resolution
https://github.com/happyhongt/Acceleration-of-RED-via-Vector-Extrapolation
The Little Engine that Could: Regularization by Denoising (RED)
https://github.com/harish678/PyTorch-Lightning-Examples/blob/master/gan_lightning.py
https://github.com/harunshimanto/Neural-Style-Transfer-of-Artistic-Style
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/havrylovv/isegprobe
FeatUp: A Model-Agnostic Framework for Features at Any Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.