For centuries, imaging devices have been based on the principle of 'pinhole projection', which directly captures the desired image. However, it has recently been shown that by co-designing the imaging optics and processing algorithms, we can obtain Computational Imaging (CI) systems that far exceed the performance of traditional cameras. Despite the advances made in designing new computational cameras, there are still many open issues such as: 1) lack of a proper theoretical framework for analysis and design of CI cameras, 2) lack of camera designs for capturing the various dimensions of light with high fidelity and 3) lack of proper use of data-driven methods that have shown tremendous success in other domains. In this talk, I will address the above mentioned issues. First, I will present a comprehensive framework for analysis of computational imaging systems and provide explicit performance guarantees for many CI systems such as light field and extended-depth-of-field cameras. Second, I will show how camera array can be exploited to capture the various dimensions of light such as spectrum and angle. Capturing these dimensions leads to novel imaging capabilities such as post-capture refocussing, hyper-spectral imaging and natural image retouching. Finally, I will talk about how various machine learning techniques such as robust regression and matrix factorization can be used for solving many imaging problems.
Kaushik Mitra is currently a postdoctoral research associate in the Electrical and Computer Engineering department of Rice University. His research interests are in computational imaging, computer vision and statistical signal processing. He earned his Ph.D. in Electrical and Computer Engineering from the University of Maryland, College Park, where his research focus was on the development of statistical models and optimization algorithms for computer vision problems