Image super resolution using with Deep Convolutional Neural Networks
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Updated
Jul 15, 2023 - Jupyter Notebook
Image super resolution using with Deep Convolutional Neural Networks
IKC: Blind Super-Resolution With Iterative Kernel Correction
HiRN: Hierarchical Recurrent Neural Network for Video Super-Resolution (VSR) using Two-Stage Feature Evolution - Official Repository (Applied Soft Computing)
Image Super-Resolution Using ESRGAN
A PyTorch implementation of ESRGAN. Additionally, a weight file trained for 200 epochs will be provided.
Group-based Bi-Directional Recurrent Wavelet Neural Network for Efficient Video Super-Resolution (VSR) - Official Repository (Pattern Recognition Letters)
This is the repository of the code related to Ruben Moyas's MSc in Data Science Master's Thesis.
The experimental implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network" ( SRGAN )
Official implementation of "MAML-SR: Self-Adaptive Super-Resolution Networks via Multi-scale Optimized Attention-aware Meta-Learning" (PRL'23)
tracking polymerization processes across a membrane through TIRF experiment data
Super-Resolution CNN using NumPy
Image restoration with neural networks but without learning.
MXNet sample for super resolution
Tensorflow 2.0 implementation of MAMNet
Video Upsampler based on Optical Flow from previous and next frames
Study and implementation of Arbitrary Scale Deep Network (https://arxiv.org/abs/2010.02414): super sampling with any-scale using a laplacian frequency representation that iteratively interpolates images with scales greater than 2.
A collection of blur kernels to generate Super Resolution datasets
All this is part of my Projektarbeit (student project) @ TU Wien 2021
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