Project Overview

  • Period: 2023.07 ~ 2023.09
  • Organization: 국방기술진흥연구소
  • Role: Research Intern at SI Analytics (SR Team)
  • Team Size: 4

Main Responsibilities

  • Research on Blind Super Resolution
    • Generating synthetic Low resolution for training in paired (hr, lr) datasets
    • Use various SR models to verify enhancement of model performance
  • Research on estimation of degradation in low resolution images (SAR & EO)
    • Use Frechet distance to estimate synthesize data and real lr data
    • Using various kinds of kernels (Noise, Blur, etc.) to estimate speckle noise in SAR datasets
    • Implement from scratch to whole pipeline to learn degradation and generate in end-to-end manners

Technical Skills

  • Python (osgeo, gdal, tifff)
  • PyTorch
  • TensorFlow
  • QGIS

Project Description

  • Develop a method for super-resolution using only real low-resolution and high-resolution images in an unpaired dataset
  • Improved existing super-resolution (SR) models by estimating unknown degradations present in Synthetic Aperture Radar (SAR) or Electro-Optical (EO) imagery
  • Implemented and experimented with a paper focused on improving super-resolution (SR) model performance