Implementation of four different deep learning models for super-resolution.
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Updated
Jul 14, 2021 - Python
Implementation of four different deep learning models for super-resolution.
Implimentation of the 2016 Fast Super Resolution CNN Paper
Project that positions an object in a video following a road lane.
Comparative study of lightweight generator models (ESPCN, FSRCNN, IDN) in the SRGAN framework for Single Image Super-Resolution (SISR). Explore the trade-offs between performance and efficiency in GAN-based SISR.
A Fast and Accurate Super-Resolution Convolutional Neural Network (FSRCNN) build for artwork, anime, and illustration.
Code for paper "Classification-based Dynamic Network for Efficient Super-Resolution"
Super Resolution using FSRCNN Dong Chao et al. paper.
A simple image upscaler application using EDSR, ESPCN, FSRCNN, and LapSRN models
Pytorch based implementation of FSRCNN for single image super-resolution
Tensorflow 2.x based implementation of FSRCNN for single image super-resolution
My personal config for MPV player with FSRCNNX shader and image viewer
An example for porting nerual network to vapoursynth.
Upscale Twitch stream and restream into Twitch or RTMP or File.
Recognition of license plate numbers, in any format, by automatic detection with Yolov8, pipeline of filters and paddleocr as OCR
Unofficial PyTorch implementation of FSRCNN (Fast Super-Resolution Convolutional Neural Network)
Tensorflow implementation of 'Accelerating the Super-Resolution Convolutional Neural Network'.
collection of super-resolution models & algorithms
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