A Caffe-based implementation of very deep convolution network for image super-resolution
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Updated
Apr 24, 2017 - MATLAB
A Caffe-based implementation of very deep convolution network for image super-resolution
Image super-resolution using matrix valued operations
Python implementation of the paper "Image Super-Resolution Using Deep Convolutional Networks" arXiv:1501.00092v3 [cs.CV] 31 Jul 2015.
Pytorch implement: Residual Dense Network for Image Super-Resolution
paper implement : Fast and Accurate Single Image Super-Resolution via Information Distillation Network
Deep learning sample code collection for MEDical image processing
Official code (Pytorch) for paper Perception-Enhanced Single Image Super-Resolution via Relativistic Generative Networks
Official code (Tensorflow) for paper "Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural Networks"
PyTorch implementation of Image Super-Resolution Using Very Deep Residual Channel Attention Networks (ECCV 2018)
PyTorch Implementation of Fast and Accurate Single Image Super-Resolution via Information Distillation Network (CVPR 2018)
Code for Non-Local Recurrent Network for Image Restoration (NeurIPS 2018)
Code of Non-Local Recurrent Network for Image Restoration (NeurIPS 2018)
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network implemented in Keras
PyTorch implementation of Image Super-Resolution Using Deep Convolutional Networks (ECCV 2014)
PyTorch implementation of Image Super-Resolution Using Dense Skip Connections (ICCV 2017)
PyTorch implementation of Image Super-Resolution via Deep Recursive Residual Network (CVPR 2017)
Camera Lens Super-Resolution in CVPR 2019
PyTorch implementation of Wide Activation for Efficient and Accurate Image Super-Resolution (CVPR Workshop 2018)
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