Computational imaging and spectroscopy

Computational imaging and spectroscopy

Traditionally, optical sensors have been designed to collect the most directly interpretable and intuitive measurements possible. For example, a standard digital camera directly measures the brightness of a scene at different spatial locations. The problem is that the combination of direct measurement systems with large or expensive (e.g., infrared) sensors typically translates into low system resolution. Recent advances in the fields of image reconstruction, inverse problems, and compressed sensing indicate, however, that substantial performance gains may be possible in some contexts via computational methods. In particular, by designing optical sensors to collect carefully chosen measurements of a scene, we can use sophisticated computational methods to infer more information about critical structure and content. As described above, photon limitations have a significant impact on the performance of computational imagers, so we face complex tradeoffs among photon efficiency (i.e., how much of the available light in harnessed), measurement diversity, and resolution. My lab has helped develop novel new computational optical systems for spectroscopy and spectral imaging that address these challenges. Our approach yields up to an 84% reduction in error from conventional approaches.