Computational imaging is a discipline at the intersection of computer vision, image processing, and optics. Its goal is to overcome the limitations of conventional imaging by combining novel imaging techniques with advanced algorithms. Modern computational imaging techniques include cameras that can capture videos at the speed of light, cameras that can see around corners or below the skin, and cameras that capture speech and music directly from the vibration they create on object surfaces.
This course will cover the basics and state-of-the-art in computational imaging. Tentative topics include:
- The modern image processing pipelines, basic optics, the image formation model (lenses, aberrations, sensor noise, and imaging in color), and computational light transport.
- Advanced image and video editing algorithms like filtering, gradient-domain processing, and deconvolution.
- Advanced image acquisition techniques like light-field imaging, coded photography, focal stacks, depth from defocus, time-of-flight imaging, 3D scanning, and more.
This course involves hands-on experience with multiple homework assignments and a final project. The homework assignments will involve capturing images and implementing some of the techniques covered in the class.