My research interests lie at the intersection of computer vision and computational photography. Computer vision relies on images and videos captured by conventional cameras. But since the discovery of the camera obscura in ancient times and up to modern electronic devices, the cameras we use have not changed much. Sure, we digitized the image and improved the optics, but the underlying imaging principle behind the modern camera mimics the human eye. Therefore, due to the particular ‘design’ of the human vision system (and machine vision as a consequence), humans and most machine vision systems cannot perceive the full range of phenomena in our surroundings.

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But why should we settle for that? Why limit the perception of machines to that of humans? Wouldn't we want our future machines, like autonomous cars and surgery robots, to have a super-human perception? To me, the answer is clear.

Therefore, my research goal is to invent imaging systems that enable vision capabilities beyond human perception. As an illustration, some of my previous works include a camera that can “see” sound vibrations on object surfaces, a camera that gathers scene data from the subtle pulsations of ubiquitous electric bulbs, a camera that senses the world in 1D but outputs 2D high-speed videos, and more. As one of my mentors told me, "the goal of computational imaging is to sense the invisible" (beyond human vision).

 

If "photography" (φωτο-γραφία) can be translated from Greek as "drawing with light", then computational photography should then be "photo-codikopoisi" (φωτο-κωδικοποίηση) or "coding with light", or perhaps "photo-logismos" (φωτο-λογισμός) which means calculating or reasoning with light*.