INNOVATIONS FROM THE EDGE - KPIT

Computational Light Transport: Ramesh Raskar,
Camera Culture, MIT
Can we photograph objects that are not in the
direct line of sight Can we build portable
machines that can see inside our body Can we
provide diagnostic care in remote parts of the
world by converting mobile phones into scientific
instruments Our research goal is to create an
entirely new class of imaging platforms that have
an understanding of the world that far exceeds
human ability, to produce meaningful
abstractions that are well within human
comprehensibility. To achieve this super-human
vision, our contributions are in new theories and
instrumentations for solving challenging inverse
problems in computational light transport. To
tackle these inverse problems, we create a
carefully orchestrated movement of photons,
measure the resulting optical response and then
computationally invert the process to learn about
the scene, so that the new imaging platforms can
achieve seemingly impossible goals.
1.1 Computational Photography
Computational photography is an emerging and
multi-disciplinary field that is at the intersection of
optics, signal processing, computer graphics and
vision, electronic hardware, visual arts, and online
sharing in social networks. At MERL and in the last
few years, we created new trends as well as original
rigorous theories by inventing unusual optics,
programmable illumination, modern sensors and
image analysis algorithms (Figure 2). With our
collaborators, we made a generalizing and
unanticipated observation that, by blocking light
over time, space, angle, wavelength or sensors, we
can reversibly encode scene information in a photo
for efficient post-capture recovery. We published
an important paper, flutter shutter camera
(Siggraph 2006), that used a binary sequence to
code exposure to deal with motion blur. This paper
(along with Fergus et al [2006]) opened a new trend
at Siggraph in papers that deal with information
loss due to blur, optical techniques and deblurring.
Our further work generalized this concept for
powerful algorithmic decomposition of a photo
into light fields (Siggraph 2007), deblurred images,
global/direct illumination components (Siggraph
2006), or geometric versus material discontinuities
(Siggraph 2004). Along the way, we also created a
new range of intelligent self-ID technologies: RFIG
(Siggraph 2004), Prakash (Siggraph 2007) and
Bokode (Siggraph 2009).
Figure 1: Our work explores creative new ways
to play with light by co-designing optical
and digital processing.
1. Towards Computational Light Transport
We have been fascinated with the idea of superhuman
abilities to visually interact with the world
via cameras that can see the unseen and displays
that can alter the sense of reality.
At MIT, we have invented two novel forms of
imaging that show immense potential for research
and practical applications: (a) time resolved
transient imaging that exploits multi-path analysis
and (b) angle resolved imaging for displays,
medical devices and phase analysis.
1.2 Computational Light Transport
At MIT, we have undertaken ambitious efforts in
creating super-human visual abilities – often
combining previously disparate fields to do things
that people thought were unattainable. New
directions include a camera that can look around
corners, portable machines that can see inside the
body and low cost sensors that can transform
healthcare around the world. We describe these in
the next section.
38 TechTalk@KPITCummins, Volume 5, Issue 1, 2012