This project shows Image Super Resolution using Deep Learning . Seven models are implemented SRCNN, FSRCNN,ESPCN, RDN, RFDN, Autoencoder and ESRGAN.

Thesis code

Replicated Results of Super Resolution Papers

Pytorch based implementation of ESPCN for single image super-resolution

Tensorflow 2.x based implementation of ESPCN for single image super-resolution

A simple image upscaler application using EDSR, ESPCN, FSRCNN, and LapSRN models

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

Implemented ESPCN with Pytorch from scratch & trained with custom dataset / Test with low-resolution video

CCF BDCI 2023 基于TPU平台实现超分辨率重建模型部署

A flow to compile ESPCN (super resolution) using TVM and run the compiled model on CPU to calculate PSNR

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.

Quang.Bui.2 - Efficacy of Diffusion Models for Synthesising Realistic Wound Images: Enhancing Wound Analysis, Training, and Education