Presented by , Associate Professor, Mathematics, Applied Mathematics, and Statistics, ÐÇ¿Õ´«Ã½
Abstract: Images in two and higher dimensions are present in our daily life, ranging from pictures taken by smartphones to those obtained from medical devices. Recent developments in science and technology have caused a revolution in the generation, acquisition, analysis, processing, and visualization of images. Take medical imaging as an example. A variety of imaging modalities, e.g., computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), provide great potential to facilitate diagnosis and treatment. On the other hand, imaging problems, including reconstruction, enhancement, segmentation, and registration, are vital to many areas of science, medicine, and engineering. However, as the explosive growth of data, traditional methods in imaging face a lot of challenges, e.g., how to reconstruct high quality/resolution images from indirect raw machine measurements, how to effectively enhance image quality, and how to extract features of interest from images. Appropriate models and efficient computational algorithms play a crucial role in imaging performance. I will present some overview of related courses we offer in the Mathematics, Applied Mathematics and Statistics department and my recent research results in these directions. The results are based on collaboration with Yue Zhang (a former PhD student, now in Siemens Corporate Research),Professors Liang-Jian Deng (a former visiting PhD student, now in UESTC, China), Jocelyn Chanussot (Grenoble Institute of Technology, Italy), Ke Chen (Liverpool, UK) and Liam Burrows (Liverpool, UK).