major challenges. He says the biggest problem is trying to compile code written in an easy-to-use, high-level language down to the hardware of a camera. He likens the challenge to the early days of shading languages and graphics chips. Shaders used to be fixed functions that programmers could manipulate in only certain limited ways. However, graphics chipmakers changed their hardware to accommodate new programming languages. As a result, says Levoy, the shader languages got better in a virtuous cycle that resulted in several languages that can now be used to control the hardware of a graphics chip at a very fine scale.
“We’d like to do the same thing with cameras,” Levoy says. “We’d like to allow a researcher to program a camera in a high-level language, like C++ or Python. I think we should have something in a year or two.”
In addition to developing an open source platform for computational photography, Levoy is working on applying some of his research in this area to microscopy. One of his projects embeds a microlens array in a microscope with the goal of being able to refocus photographs after they are taken. Because of the presence of multiple microlenses, the technology allows for slightly shifting the viewpoint to see around the sides of objects— even after capturing an image. With a traditional microscope, you can of course refocus on an object over time by moving the microscope’s stage up and down. But if you are trying to capture an object that is moving or a microscopic event that is happening very quickly, being able to take only a single shot before the scene changes is a serious drawback. In addition to Levoy’s microlens array allowing images to be refocused after they are captured, the technology also offers the ability to render objects in three dimensions. “Because I can take a single snapshot and refocus up and down,” he says, “I can get three-dimensional information from a single instant in time.”
These and other developments in computational photography are leading to a vast array of new options for researchers, industry, and photography enthusiasts. But as with any advanced technologies that have interfaces designed to be used by humans, one of
the major challenges for computational photography is usability. While computer chips can do the heavy lifting for many of these new developments, the perception that users must work with multiple images or limitless settings to generate a good photo might be a difficult barrier to overcome. Levoy points to the Casio EX-F1 camera as a positive step toward solving this usability problem. He says the EX-F1, which he calls the first computational camera, is a game changer. “With this camera, you can take a picture in a dark room and it will take a burst of photos, then align and merge them together to produce a single photograph that is not as noisy as it would be if you took a photograph without flash in a dark room,” he says. “There is relatively little extra load on the person.”
Levoy predicts that cameras will follow the path of mobile phones, which, for some people, have obviated the need for a computer. “There are going to be a lot of people with digital cameras who don’t have a computer and never will,” he says. “Addressing that community is going to be interesting.” He also predicts that high-end cameras will have amazing flexibility and point-and-shoot cameras will take much better pictures than they do now. Nayar is of a similar opinion. “One would ultimately try to develop a camera where you can press a button, take a picture, and do any number of things with it,” he says, “almost like you’re giving the optics new life.”
based in los angeles, Kirk L. Kroeker is a freelance editor and writer specializing in science and technology.
ibM has received a $4.9 million grant from DaRPa to lead an ambitious, cross-disciplinary research project to create a new computing platform: electronic circuits that operate like a brain.
“the mind has an amazing
ability to integrate ambiguous
information across the senses,
and it can effortlessly create
the categories of time, space,
object, and interrelationship
from the sensory data,”
Dharmendra Modha, the ibM
researcher who is leading the
project, told bbc news. “the
key idea of cognitive computing
is to engineer mind-like
intelligent machines by reverse
engineering the structure,
dynamics, function, and
behavior of the brain.”
the cognitive computing
initiative will include
neuroscientists, computer
and materials scientists, and
psychologists from ibM and
five U.S. universities. Modha
believes the time is right for
the project as neuroscientists
have learned a great deal about
the inner workings of neurons
and their connecting synapses,
resulting in “wiring diagrams”
for the brains of simple
animals. also, supercomputing
is able to simulate brains
up to the complexity level of
small mammals; last year a
Modha-led ibM team used its
blue gene supercomputer to
simulate the 55 million neurons
and half-a-trillion synapses in
a mouse’s brain. “but the real
challenge is then to manifest
what will be learned from future
simulations into real electronic
devices—nanotechnology,”
according to Modha.
along with ibM almaden
Research center and ibM t.
J. Watson Research center, Stanford University, University of Wisconsin-Madison, cornell University, columbia University Medical center, and University of california-Merced are participating in the project.
Modha acknowledges that the project is very ambitious in terms of its scope and goals. “We are going not just for a home run,” he said, “but for a home run with the bases loaded.”
feBRuaRY 2009 | vol. 52 | No. 2 | CommunICatIons of the aCm
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