Fall 2016 18-793 Image and Video Processing

This course covers signal processing techniques specialized for handling 2D (images) and 3D (videos) signals. It builds upon 1D signal processing techniques developed in 18-290 and 18-491 and specializes them for the case of images and videos. In this class, you will learn fundamental tools and techniques for processing images and videos, and will learn to apply them to a range of practical applications.

Spring 2016 18-290 Signals and systems

This course develops the mathematical foundation and computational tools for processing continuous-time and discrete-time signals in both time and frequency domain. Key concepts and tools introduced and discussed in this class include linear time-invariant system, impulse response, frequency response, convolution, filtering, sampling, and Fourier transform. The course provides backgorund to a wide range of applications including speedch, image, and multimedia processing, bio and medical imaging, sensor networks, communication systems, and control systems.

Fall 2015
18-799J Compressive sensing and sparse optimization

In this course, we develop a formal study of techniques required to solve under- determined linear systems. We discuss the theory and practice of sparse optimization. This involves a rich interplay of ideas from linear algebra, probability, and convex optimization. Finally, we will explore broader applications in computational imaging, neural signal processing, and sparse regression that benefit from the theory and optimization methods developed in the course.

Spring 2014 18-799N Computational sensors

While there has been tremendous technological leaps in the design of modern cameras, their basic construction more or less remains the same as that of the pinhole camera. The last few decades have seen the development of computational imaging sensors --- systems whose design are inspired by the specifics of the problem they intend to solve. At the heart of these computational sensors is the co-design of optics and processing for improved information throughput. This course starts with understanding the fundamentals of vision sensors - how they function, how they are built, the inherent tradeoffs, and how to use them effectively. Starting from here, we will study a how computational sensor designs alter these basic tradeoffs to provide enhanced solutions for a range of imaging applications including high-speed, high dynamic range and multi-spectral imaging, 3D acquisition, and programmable imaging.

Fall 2013 18-290 Signals and systems

Spring 2013 18-799J Compressive sensing and sparse optimization