Research on AI Technology for the Efficient Real-Time Object Detection using super-resolution.
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Updated
Jan 3, 2023 - Python
Research on AI Technology for the Efficient Real-Time Object Detection using super-resolution.
This project is about the fundamentals of super-resolution artificial intelligence, specifically centered on the Enhanced Deep Super-Resolution (EDSR) technique. EDSR is used to improve image quality and resolution. The project includes a practical example demonstrating how a basic EDSR model works in enhancing images.
This is an Image Enhancement project which uses EDSR, WDSR, and SRGAN methods to increase the resolution of an image while also improving the details significantly.
Deep.Imager - A Python based app powered by Streamlit! Upscales images using mathematical convolutions and Deep Learning without change/loss in input sharpness
Martialis - Optical Character Recognition Discord Bot running on Google's Tesseract OCR Engine
PyTorch implementation of SRCNN and EDSR neural networks for Super Resolution Single Frame tasks
A simple image upscaler application using EDSR, ESPCN, FSRCNN, and LapSRN models
This repository is an implementation of EDSR model implemented in PyTorch
Super Resolution using EDSR. Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR) model trained to convert a Low-Resolution image to a Super-Resolution image.
EDSR with depthwise separable convolution
Can we perform face hallucination using limited set of unaligned pairs?
A tensorflow-based implementation of SISR using EDSR, SRResNet, and SRGAN
Super-Resolution models implemented in PyTorch Lightning
PyTorch implementation of Deep Convolution Networks based on EDSR for Compression(Jpeg) Artifacts Reduction
Super Resolution datasets and models in Pytorch
TensorFlow implementation of 'Enhanced Deep Residual Networks for Single Image Super-Resolution'.
Fast stable diffusion on CPU
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