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/A-Mulvihill/TCD-Computer-Vision-Project-2023
https://github.com/AD2605/Single-Image-Super-Resolution
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/Aal-Izz-Well/pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/Aanisha/SuperResolution
https://github.com/AbdulMoqeet/MAFFSRN
Multi-Attention Based Ultra Lightweight Image Super-Resolution
https://github.com/AbhinayReddyYarva/SuperResolution
https://github.com/AdivarekarBhumit/MAANet
MAANet: Multi-view Aware Attention Networks for Image Super-Resolution
https://github.com/Adk2001tech/GAN-Image-Super-Resolution
Super-Resolution with Deep Convolutional Sufficient Statistics
https://github.com/AdneneBoumessouer/Anomaly-Detection
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/AdneneBoumessouer/LEGO-Anomaly-Detection
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/AdneneBoumessouer/MVTec-Anomaly-Detection
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/Akella17/Voice_Style_Transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Algolzw/EBSR
https://github.com/Alina9/Style-transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/AloneGu/keras_tf_neural_img
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Amritha16/ImageResolutionEnhancement
https://github.com/Amrou7/Sin-GAN-master
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/AndersonCotrim/SBFBurst
https://github.com/AniketP04/EnhanceNet-Single-Image-Super-Resolution-Through-Automated-Texture-Synthesis
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
https://github.com/AniketVashishtha/SUPER_RESOLUTION_DL
https://github.com/AnjanaGJoseph/Super-Resolution-GAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/Anou9531/Bayesian-CNN
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
https://github.com/AntonioAlgaida/Edge.SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Apogentus/style-transfer-with-strength-control
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Aqsa-K/SRGAN-Keras
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Araxeus/PNG-Upscale
Accelerating the Super-Resolution Convolutional Neural Network
https://github.com/ArchieMeng/realsr-ncnn-vulkan-python
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
https://github.com/Ash-Lee233/IRN
https://github.com/AtharvBhat/Plus-Ultra
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/AureliePeng/Keras-WaveletTransform
https://github.com/BearNinja123/esrGAN_vBearNinja123
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Build-Week-Spotify-Song-Suggester-5/Data-Science
https://github.com/CREVIOS/MediSinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/CYetlanezi/Proyecto-Opti
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Capstone-Projects-2019-Fall/GAN_Image_Editor
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.