Much to the chagrin of FiO attendees, Tuesday morning started with gusty winds and rain. I and my friend had moved to a hotel 1 mile away from the conference venue. Deciding the best way to reach the venue in the down-pour took time and we missed Ramesh Raskar’s talk on computational photography 
After finally making to the venue, I attended talks related to computational photography and microscopy, phase-space analysis and phase-retrieval. The trend I am likely to continue for the rest of the two days. There were quite a few I had to miss due to overlap. FiO is a great place to gather ideas from diverse backgrounds. I attended talks from computer scientists designing new computational photography methods, optical microscopists designing computational microscopy methods based on SLM, mathematicians pondering at basics of compressive sensing and phase-space analysis, and astronomers trying to retrieve phase-information about medium between earth and heavenly bodies. Such a mix has to be hard to find elsewhere.
It is interesting to ponder upon how these fields are inter-related: Computational microscopy and photography methods solve problems that are hard to solve with the traditional scheme of ‘acquire first and process later’. Some of these techniques benefit from acquiring light-fields rather than images. Light-field is the distribution of intensity along space and angle and as we know an image is the distribution of intensity only along space. This notion makes it attractive for researchers in computational work to adopt phase-space analysis, which in effect describes optical phenomena in space and angle dimensions. Even though physical acquisition of light-fields may not be involved, there are problems where computation in phase-space *enables* processing tasks that cannot be performed otherwise. After one has acquired non-image but information-rich light-field (or other representation), one is faced with the task of recovering useful information. This is where reconstructions in computational methods and traditional phase-retrieval approaches from optics have parallels.
Let me note just one example from each field that illustrates above connections. These examples were picked because I relatively understood these ideas better than the others.
In the morning session, Fredo Durand (session: CTuB1), a computer scientist from MIT, gave an interesting talk about how one can think of variation of intensities in space and time in terms of light-fields. He took an example of deblurring one dimensional motion, which could be deblurred by imposing parabolic motion on the camera itself.
Immediately following that talk was the talk by Markus Testorf (CTuB2) in which he described how phase-space analysis can be used in design of computational imaging systems. He revived a notion of ‘The instrument function’ which describes what region of phase-space is acquired by an instrument.
In the afternoon was the talk by Monica Ritsch-Marte (FTuU1), in which she described several ways in which a spatial light modulator can be used in microscopic systems to design novel contrasts and to achieve phase-retrieval. Determining patterns that should be put on SLM to achieve certain point spread function of the imaging system and carrying out phase-retrieval requires use of algorithms developed in astronomy (e.g., Gerchberg – Saxton algorithm).
