collection of super-resolution models & algorithms
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Updated
Dec 20, 2018 - Python
collection of super-resolution models & algorithms
Tensorflow implementation of 'Accelerating the Super-Resolution Convolutional Neural Network'.
Unofficial PyTorch implementation of FSRCNN (Fast Super-Resolution Convolutional Neural Network)
Recognition of license plate numbers, in any format, by automatic detection with Yolov8, pipeline of filters and paddleocr as OCR
Upscale Twitch stream and restream into Twitch or RTMP or File.
An example for porting nerual network to vapoursynth.
Tensorflow 2.x based implementation of FSRCNN for single image super-resolution
Pytorch based implementation of FSRCNN for single image super-resolution
A simple image upscaler application using EDSR, ESPCN, FSRCNN, and LapSRN models
🌃 Comparing AI super-resolution models with visual examples and runtime analysis for image upscaling and restoration.
Super Resolution using FSRCNN Dong Chao et al. paper.
Video super-resolution with FSRCNN (PyTorch), benchmarked against classical upscaling on DIV2K and Vimeo-90K
Code for paper "Classification-based Dynamic Network for Efficient Super-Resolution"
A Fast and Accurate Super-Resolution Convolutional Neural Network (FSRCNN) build for artwork, anime, and illustration.
Project that positions an object in a video following a road lane.
PyTorch implementation of FSRCNN (Fast Super-Resolution CNN). Hourglass architecture, VGG-16/19 Perceptual Loss, custom data pipelines for BSD-100, Set-5, Set-14, Urban100.
FSRCNN super-resolution implementation with patch datasets, PSNR evaluation, and bicubic comparison.
Implementation of four different deep learning models for super-resolution.
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