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.

CCF BDCI 2023 基于TPU平台实现超分辨率重建模型部署

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

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

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

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

Pytorch based implementation of ESPCN for single image super-resolution

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

Thesis code

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

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

Replicated Results of Super Resolution Papers