My first Deep Learning Project. A small project on SRCNN (Super Resolution Convolutional Neural Network) for image enhancement/image restoration.)
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Updated
Aug 12, 2024 - Jupyter Notebook
My first Deep Learning Project. A small project on SRCNN (Super Resolution Convolutional Neural Network) for image enhancement/image restoration.)
Scalable Super Resolution in pure rust
AI-powered Image Resize Tool
Upscale an illustration and increase details
Super-Resolution models implemented in PyTorch Lightning
Image super resolution using with Deep Convolutional Neural Networks
TensorFlow implementation of SRCNN
This project implements SRCNN (Super-Resolution Convolutional Neural Network) for single-image super-resolution. The algorithm is trained on a dataset of low-resolution and high-resolution image pairs, and can improve the visual quality of low-resolution images by generating high-resolution images from them.
Critical Analysis and Implementation of SRCNN (part of Deep Learning and Computer Vision module)
Deep Learning-based super resolution image reconstruction
A Super-Resolution Convolutional Neural Network builds for artwork, anime, and illustration. Senior Project - Artwork Enlargement and Quality Improvement using Machine Learning. ICITEE 2021 - Enhancement of Anime Imaging Enlargement Using Modified Super-Resolution CNN.
A Super Sampling model created using the SRCNN method proposed by Chao Dong, Chen Change Loy in 2015. It uses Convolutional Networks to identify features and uses "Depth-To-Feature" technique in the end to generate a high resolution image of a given low resolution input. The model is trained and tested on BSDS500 dataset.
Pytorch based implementation of SRCNN for single image super-resolution
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