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/jiny2001/deeply-recursive-cnn-tf
Deeply-Recursive Convolutional Network for Image Super-Resolution
https://github.com/jixiaozhong/RealSR
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
https://github.com/jmalhot/SuperResolution-Computer-Vision-Using-GAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/jmrf/dcscn-super-resolution
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network
https://github.com/josiahwsmith10/Efficient-3-D-Near-Field-MIMO-SAR-Imaging-for-Irregular-Scanning-Geometries
A Vision Transformer Approach for Efficient Near-Field Irregular SAR Super-Resolution
https://github.com/jpatts/srgan_tf
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/jshermeyer/RFSR
The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery
https://github.com/jshermeyer/VDSR4Geo
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
https://github.com/jsll/pytorch_6dof-graspnet
Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation
https://github.com/junhocho/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/junjun-jiang/RefESR
https://github.com/jupiterman/Super-Resolution-Images
https://github.com/kadirnar/bsrgan-pip
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
https://github.com/kailigo/pacl
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/kainoj/keep-pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/kdhRick2222/Exposure-slot
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems
https://github.com/keerthan2/RDN-Tensorflow
https://github.com/keerthan2/Residual-Channel-Attention-Network
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/kfallah/NODE-Denoiser
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/kiakh93/C-GAN
https://github.com/kingcheng2000/GAN
https://github.com/kingcong/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/kingcong/csd
Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
https://github.com/kingcong/gpu_ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/kingsj0405/pulse-any-image
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/kli8996/HISR-Spectral-Mixup-Heterogeneous-Datasets
Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous Datasets
https://github.com/kongdebug/RCAN-Paddle
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/krantirk/Self-Supervised-photo
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/krasserm/super-resolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/kristerseiya/SinGAN_Implementation
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/ksivaman/super-res
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/kumar-shridhar/BayesianConvNet
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
https://github.com/kunjan-mhaske/Image-super-resolution-model-using-4-layers-CNN-from-PyTorch
https://github.com/kunzmi/ImageStackAlignator
https://github.com/kvsnoufal/neural_style_transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.