https://github.com/tudelft-iv/occluded_vehicle_acoustic_detection
Hearing What You Cannot See: Acoustic Vehicle Detection Around Corners
Papers with Code 아카이브
Image Super-Resolution
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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/tum-vision/learn_prox_ops
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/turboLIU/SRFBN-tensorflow
https://github.com/tuvovan/RFDNet-ImageSuperResolution
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
https://github.com/tuvovan/ZSSR-Keras
https://github.com/twhui/SRGAN-PyTorch
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/twice154/ofa-for-super-resolution
Learned Image Downscaling for Upscaling using Content Adaptive Resampler
https://github.com/twtygqyy/pytorch-SRResNet
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/u7javed/Image-Enhancer-via-ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/u7javed/Image-Super-Resolution-Enhancer-via-ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/ubombar/super-resolution-srrnet
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/ucbdrive/3d-vehicle-tracking
https://github.com/valfride/lpsr-lacd
Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers
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/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/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/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/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/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/woutercools1998/SRResNet
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/wwhio/megmodels
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.