The Taub Faculty of Computer Science Events and Talks

Hyperspectral imaging allows measuring the spectrum of each pixel in the image of a scene. This modality has diverse applications, ranging from agriculture and geoscience, to biomedical imaging and surveillance. However, most existing methods require dedicated cameras, which make use of extra optical elements (prisms, fibers, lenslet arrays, etc.). These solutions are often expensive and cumbersome to deploy in many settings. In this work, we introduce a simple hyperspectral imaging approach, which uses a conventional camera without any special optical components. Our method exploits the fact that different wavelengths have different Point Spread Functions (PSFs), and thus experience different blurs. We prove that this cue alone is insufficient for fully recovering the spectrum of each pixel, even if the locations of edges in the (sharp) image are precisely known. Yet, we show that if the spectrum at some of the pixels is known, then the inherent ambiguity can be fully resolved. Our approach thus consists of holding a black mask in front of the object we wish to image. Our algorithm automatically detects the black pixels corresponding to the mask, and uses their known spectrum to estimate the spectra of all other pixels. Extensive simulations confirm the flexibility and effectiveness of our method.

*M.Sc. research under supervision of Prof. Tomer Michaeli.