https://github.com/liqichen6688/baycnn
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
Papers with Code 아카이브
Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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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/liruilong940607/SRResnet
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/littlebeen/GCRDN
TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging
https://github.com/liuzhen03/ADNet
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/liyuantsao/flowsr-lp
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/lizatish/My_CNN
The 2018 PIRM Challenge on Perceptual Image Super-resolution
https://github.com/lizhuoq/ESRGAN-pytorch
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/locuslab/ect
https://github.com/lonnyzhang423/github-hot-hub
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/lorenzo-stacchio/Stable-Diffusion-Inpaint
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/lorenzomammana/MSLapSRN-pytorch
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks
https://github.com/lshaw8317/mlmcfordms
https://github.com/lszhuhaichao/Deep-Video-Deinterlacing
https://github.com/lucasmfaria/Machine-Learning-Capstone
https://github.com/lucidrains/lumiere-pytorch
https://github.com/lucidrains/make-a-video-pytorch
Make-A-Video: Text-to-Video Generation without Text-Video Data
https://github.com/lvyufeng/MindSpore-GAN
https://github.com/lxy5513/Multi-Style-Transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/lyeoni/pytorch-mnist-GAN
https://github.com/lyqcom/dbpn
Deep Back-Projection Networks for Single Image Super-resolution
https://github.com/lyqcom/rednet30
https://github.com/lzhengchun/deep-image-prior-tensorflow
https://github.com/macn3388/MCAN
A Matrix-in-matrix Neural Network for Image Super Resolution
https://github.com/maiyuxiaoge/srgan
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/maminio/sample-generating
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/mandalinadagi/Comp541-DeepLearning
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/manricheon/MAMNet-Tensorflow-2
MAMNet: Multi-path Adaptive Modulation Network for Image Super-Resolution
https://github.com/manricheon/eusr-pcl-tf
Generative adversarial network-based image super-resolution using perceptual content losses
https://github.com/manricheon/manricheon.github.io
Lightweight and Efficient Image Super-Resolution with Block State-based Recursive Network
https://github.com/maosnhehe/RIPR
https://github.com/marcin-laskowski/Pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/markmaxt/VideoSR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/marload/GANs-TensorFlow2
https://github.com/marload/TensorFlow2-GANs
https://github.com/matciotola/fast-z-pnn
Pansharpening by convolutional neural networks in the full resolution framework
https://github.com/matt-bendel/pcagan
A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.