https://github.com/Lornatang/VDSR-PyTorch
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Papers with Code 아카이브
Super-Resolution
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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/Lucien66/LIPT
https://github.com/MChaus/NeoRender_test_task
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
https://github.com/MINGUKKANG/Adversarial-AutoEncoder
https://github.com/MLI-lab/Robustness-CS
https://github.com/MLX15/TecoGAN
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/MNaseerSubhani/MVTec-Anomaly-Detection-For-Industry
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/MS-Mind/MS-Code-06/tree/main/Pix2Pix
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Mandroide/gpasb-nvidia2021-track4
Good Practices and A Strong Baseline for Traffic Anomaly Detection
https://github.com/MarkPrecursor/SRCNN-keras
https://github.com/MartinBuessemeyer/Artistic-Texture-Control
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Martinchen2015/VideoDDtest
Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors
https://github.com/Martion-z/Paddle-PULSE
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/Martion-z/pytorch-Pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/MathieuRita/SinGAN_styletransfer
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/MatusPilnan/nsiete-project
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/MaxHolmberg96/Fast-super-resolution-by-CNN
Accelerating the Super-Resolution Convolutional Neural Network
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Deep_Image_Prior
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Generative_Adversarial_Networks
https://github.com/MediaTek-NeuroPilot/mai21-learned-smartphone-isp
Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and Latency
https://github.com/MehranSoltani94/ChannelNet
https://github.com/MichalKacprzak99/reconstruction_particle_mass_spectra
https://github.com/Michel-liu/FatFormer
CNN-generated images are surprisingly easy to spot... for now
https://github.com/Mind23-2/MindCode-101/tree/main/IPT
https://github.com/Mind23-2/MindCode-101/tree/main/IRN
https://github.com/Mind23-2/MindCode-174
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/Mind23-2/MindCode-2/tree/main/wdsr
Wide Activation for Efficient and Accurate Image Super-Resolution
https://github.com/Mind23-2/MindCode-3/tree/main/HiFaceGAN
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
https://github.com/Mind23-2/MindCode-3/tree/main/IPT
https://github.com/Mind23-2/MindCode-3/tree/main/IRN
https://github.com/Mind23-2/MindCode-5/tree/main/RCAN
Revisiting RCAN: Improved Training for Image Super-Resolution
https://github.com/Mind23-2/MindCode-5/tree/main/RDN
https://github.com/Mind23-2/MindCode-5/tree/main/rbpn
Recurrent Back-Projection Network for Video Super-Resolution
https://github.com/Mind23-2/MindCode-78
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Mind23-2/MindCode-81
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Mind23-2/MindCode-88/tree/main/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.