Faculty: Murat Tekalp (mtekalp@ku.edu.tr)
Description:
The huge success of deep-learning-based approaches in computer vision inspired research in learned solutions to classic image/video processing problems, such as denoising, deblurring, super-resolution, and compression. Hence, deep learning based methods have emerged as a promising nonlinear signal processing framework for image/video restoration and compression. Recent works have shown that learned models can achieve significant performance gains over traditional methods. Hence, the state of the art in image restoration and compression is getting redefined. Yet, compelling research challenges still remain to be addressed. These include:
iiii) the performance of learned models trained on synthetically generated data drops sharply on real-world images/video, where the quantity and quality of training data is limited. and
The successful candidate will conduct innovative research to address these and other challenges. Topics of interest include (but are not limited to):