https://github.com/kynk94/TF2-Image-Generation
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Papers with Code 아카이브
Image Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 평가표
- 69
- 보존된 평가 기록
- 748
Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/l3th4l/SuperRes
https://github.com/laowng/GISR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/lee-aaron/Image-Super-Resolution
https://github.com/leehomyc/Photo-Realistic-Super-Resoluton
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/leftthomas/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/leftthomas/espcn
https://github.com/libreai/neural-painters-x
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/linxi159/GAN-training-tricks
https://github.com/linxi159/Tips-and-tricks-to-train-GANs
https://github.com/liqichen6688/baycnn
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
https://github.com/liruilong940607/SRResnet
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
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/lorenzomammana/MSLapSRN-pytorch
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks
https://github.com/lshaw8317/mlmcfordms
https://github.com/lxy5513/Multi-Style-Transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/lyqcom/dbpn
Deep Back-Projection Networks for Single Image Super-resolution
https://github.com/lyqcom/rednet30
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/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/matciotola/fast-z-pnn
Pansharpening by convolutional neural networks in the full resolution framework
https://github.com/mayank-17/Project-Image-Restoration-using-SRCNN
https://github.com/mcordts/cityscapesScripts
Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection
https://github.com/med-seg/kidney
https://github.com/med-seg/kidney-mc
https://github.com/media2x/realsr-ncnn-vulkan-python
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
https://github.com/megvii-research/TLC
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.