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/valentingol/GANJax
https://github.com/vamsi3/simple-GAN
https://github.com/vandit15/SRCondenseNet
Efficient Single Image Super Resolution using Enhanced Learned Group Convolutions
https://github.com/vcaptainv/SinGan-Data-Augumentation
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/vcaptainv/SinGan-video_texture
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/ved27/RED-net
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/venkat2319/Mirnet
Learning Enriched Features for Real Image Restoration and Enhancement
https://github.com/venkycode/re-singan
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/victorruelle/SinGAN-extension
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/vieduy/Neural-Style-Transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/vijishmadhavan/ArtLine
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/vijishmadhavan/SkinDeep
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/vijishmadhavan/Toon-Me
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/vishwa91/wire
https://github.com/vision-xl/codes
VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models
https://github.com/vita-group/gan-slimming
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
https://github.com/vpaliwal1/Deep_learning_SRCNN
https://github.com/vscoboy/AI-
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/vskulkarni31/proSR
A Fully Progressive Approach to Single-Image Super-Resolution
https://github.com/vskulkarni31/srgan
A Fully Progressive Approach to Single-Image Super-Resolution
https://github.com/vuanhtu1993/Keras-SRGANs
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/w28ahmad/FaceGen
https://github.com/wackygerbs/NAS-DIP
https://github.com/wayne1123/mnist_wgan_gp
https://github.com/waze96/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/wdd233/RCAN
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/wenhaoding/pino-cde
Physics-Informed Neural Operator for Learning Partial Differential Equations
https://github.com/wenyuqing/panacea
Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving
https://github.com/wh1h/DNLN
https://github.com/wileyw/DeepLearningDemos
Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
https://github.com/wilile26811249/Style_Transfer_PyTorch
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/will-wang19/Image-Edit-with-Mask-generated-by-Stable-Diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/wkhademi/ImageEnhancement
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/wnorowsb/Super-resolution-GAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/woshisad159/tfnet
Continuous Sign Language Recognition via Temporal Super-Resolution Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.