Current synthetic aperture radar (SAR) image recognition systems suffer from significant degradation when the systems are trained with synthetic images but tested with real measured images. To address this issue, the project is aimed at developing a quasi-supervised-learning approach for SAR image recognition. The key idea is transfer learning with quasi-supervised training procedures.
Tag: RADAR Systems
M3-V Privacy-Preserving Fall Detection with Deep Learning on mmWaveRadar Signal (renamed: Low-Light and Low-Resolution Image Enhancement)
This project aims to design a deep learning based end-to-end solution for dark image denoising and enhancement using image decomposition method, achieving significant improvement in output image quality. Further it aims to improve the image super-resolution by exploiting temporal redundancy. The project also aims at developing an end-to-end solution for multi-frame image super-resolution which involves image registration using optical flow and deep learning for noise removal, achieving significant gain in performance over the state-of-the-art methods.