https://github.com/SJHNJU/wdsr_b
Wide Activation for Efficient and Accurate Image Super-Resolution
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/Saafke/EDSR_Tensorflow
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/SeungyounShin/CAGFace
Component Attention Guided Face Super-Resolution Network: CAGFace
https://github.com/ShakedDovrat/JpegArtifactRemoval
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/Shakib-IO/Diminishing_Image_Noise_Using_Deep_Learning
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/ShambhawiVarchasva/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Shritesh99/100DaysofMLCodeChallenge
https://github.com/Shritesh99/Image_Super_Resolution
https://github.com/ShutoAraki/EverybodyDanceNow-Temporal-FaceGAN
https://github.com/SimoneDutto/EDSR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/Slayerxxx/E-RAISR
Brain MRI Image Super Resolution using Phase Stretch Transform and Transfer Learning
https://github.com/SofiaBlinova/EDSR-net
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/Suanmd/TR-MISR
https://github.com/THINKWARE-AI/NCAP
NCAP: Scene Text Image Super-Resolution with Non-CAtegorical Prior
https://github.com/TahmasbiM/Example
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/TanakitInt/SRCNN-anime
https://github.com/TanguyJeanneau/white-mirror
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/TanyaChutani/Image-Super-Resolution-SRGAN-TF2.0
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Tee0125/pytorch-fast-style-transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/TingdiRen/URSCT-SESR
Underwater Image Super-Resolution using Deep Residual Multipliers
https://github.com/TomokiKomiya/SRGAN-keras
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/TottiPuc/Hyperspectral_ImageReconstruction_GAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/TuSimple/RangeDet
RangeDet:In Defense of Range View for LiDAR-based 3D Object Detection
https://github.com/UBERCRUZER/superresolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/UdbhavPrasad072300/GANs-Implementations
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/UdbhavPrasad072300/Image-Super-Resolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/V0LsTeR/mySRResNet
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/V0LsTeR/mySuperRes
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/VIGNESHinZONE/Face-Super-Resolution-Through-Wasserstein-GANs
https://github.com/VisDrone/DroneVehicle
Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning
https://github.com/WalterJohnson0/tf-keras-implementation-of-Image-Style-transformation-network
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/WarClans612/deep_learning_final
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/WarrenGreen/srcnn
https://github.com/WeiHan3/dsrn
https://github.com/Weifeng73/Zero-Shot-Super-resolution
https://github.com/WenlongZhang0724/RankSRGAN
RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.