Compiled code of many super resolution models, starting from simple to more advanced architectures. All models trained on MSCOCO 2017 data.
- Autoencoder
- ResNet
- UNet
- SRResNet
- SRGAN
- Deep Residual Learning for Image Recognition
- U-Net: Convolutional Networks for Biomedical Image Segmentation
- Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
- Enhanced Deep Residual Networks for Single Image Super-Resolution
- Perceptual Losses for Real-Time Style Transfer and Super-Resolution
- Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
- Kaggle: Image Super-Resolution using Autoencoders
- GitHub: Image Super-Resolution using Autoencoders
- Medium: Super Resolution using Autoencoders and TF2.0
- Cedrick Chee: Knowledge Courses
- Towards Data Science: Deep Learning Based Super-Resolution Without Using a GAN
- GitHub: Super Resolution DNN