https://github.com/LabForComputationalVision/bias_free_denoising
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
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/Labyz/SinGAN-MVA
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Leozhibin/PReNet-master
Progressive Image Deraining Networks: A Better and Simpler Baseline
https://github.com/LongguangWang/ArbSR
Learning A Single Network for Scale-Arbitrary Super-Resolution
https://github.com/LongguangWang/Scale-Arbitrary-SR
Learning A Single Network for Scale-Arbitrary Super-Resolution
https://github.com/Lornatang/CSNLN-PyTorch
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/Lornatang/DRRN-PyTorch
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
https://github.com/Lornatang/ESPCN-PyTorch
https://github.com/Lornatang/ESRGAN-PyTorch
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Lornatang/FSRCNN-PyTorch
https://github.com/Lornatang/Meta_RDN-PyTorch
Meta-SR: A Magnification-Arbitrary Network for Super-Resolution
https://github.com/Lornatang/RFB_ESRGAN-PyTorch
Perceptual Extreme Super Resolution Network with Receptive Field Block
https://github.com/Lornatang/SFTMD-PyTorch
https://github.com/Lornatang/SRCNN-PyTorch
https://github.com/Lornatang/SRGAN-PyTorch
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Lornatang/SRGAN_PyTorch
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Lornatang/VDSR-PyTorch
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
https://github.com/Lucien66/LIPT
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/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/MehranSoltani94/ChannelNet
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.