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 )
The goal is to understand whether Person Verification works better with a preliminary application of Super-Resolution or not
Tensorflow 2.0 implementation of Accurate Image Super-Resolution Using Very Deep Convolutional Networks (CVPR 2016) with jupyter notebook.
動漫插畫放大/降噪
An adversarial algorithm for generating super resolution of images
An Accurate Extraction of Facial Meta-Information Using Selective Super Resolution from Crowd Images
Implementation of the paper "Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data." 🖼️
Machine Learning - Super Resolution
Harmonics' Radius Index (HRI95) is a full-reference image quality index based harmonic structures of the images for the comparison super-resolution models. This version is the newest one.
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
Python implementation for Mean Shift Super Resolution algorithm for images in 3 dimensions (Under development).
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