Torch implementation of "Enhanced Deep Residual Networks for Single Image Super-Resolution"
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Updated
Jan 6, 2018 - Lua
Torch implementation of "Enhanced Deep Residual Networks for Single Image Super-Resolution"
EDSR with depthwise separable convolution
PyTorch implementation of SRCNN and EDSR neural networks for Super Resolution Single Frame tasks
PyTorch implementation of Deep Convolution Networks based on EDSR for Compression(Jpeg) Artifacts Reduction
This repository is an implementation of EDSR model implemented in PyTorch
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.
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.
A simple image upscaler application using EDSR, ESPCN, FSRCNN, and LapSRN models
A tensorflow-based implementation of SISR using EDSR, SRResNet, and SRGAN
Implementation of EDSR, ESPCN, LAPSRN, SRCNN, SRGAN and WDSR for single image super-resolution (SISR) based on Tensorflow 2.x for CMU's 10-707 Advanced Deep Learning Final Project
Tensorflow 2.x based implementation of EDSR, WDSR and SRGAN for single image super-resolution
single image super resolution
Image Classification
ERNet: Segmentation of Endoplasmic Reticulum microscopy images using modified CNN-based image restoration models.
Research on AI Technology for the Efficient Real-Time Object Detection using super-resolution.
TensorFlow implementation of 'Enhanced Deep Residual Networks for Single Image Super-Resolution'.
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