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
Image Super-Resolution Using ESRGAN
ESRGAN
A PyTorch implementation of ESRGAN. Additionally, a weight file trained for 200 epochs will be provided.
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 )
This is a python package to perform progressive refinement method for sparse recovery (PRIS)
Számítógépes képfeldolgozás
Unofficial implementation of NCNet using flax and jax
FSSBP: Fast Spatial–Spectral Back Projection Based on Pan-Sharpening Iterative Optimization
Deep Learning Super Sampling with Deep Convolutional Generative Adversarial Networks.
Python implementation for Mean Shift Super Resolution algorithm for images in 3 dimensions (Under development).
The repository is for Skoltech Master's thesis work on "GAN-based Multi-Image Super-Resolution for Remote Sensing Imagery"
Image restoration with neural networks but without learning.
All this is part of my Projektarbeit (student project) @ TU Wien 2021
AI4EO challenge
Construct an Efficient Sub-Pixel Convolutional Neural Network in Python for Image Super Resolution
"Learning with Image Guidance for Digital Elevation Model Super-Resolution" implementation
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