Forum for Science, Industry and Business

Sponsored by:     3M 
Search our Site:

 

System automatically retouches cellphone images in real-time

03.08.2017

New system can apply a range of styles in real-time, so that the viewfinder displays the enhanced image.

The data captured by today's digital cameras is often treated as the raw material of a final image. Before uploading pictures to social networking sites, even casual cellphone photographers might spend a minute or two balancing color and tuning contrast, with one of the many popular image-processing programs now available.


Researchers describe a new system that can automatically retouch images in the style of a professional photographer. It can run on a cellphone and it's so fast that it can display retouched images in real-time, so that the photographer can see the final version of the image while still framing.

Courtesy of the researchers (edited by MIT News)

This week at Siggraph, the premier digital graphics conference, researchers from MIT's Computer Science and Artificial Intelligence Laboratory and Google are presenting a new system that can automatically retouch images in the style of a professional photographer. It's so energy-efficient, however, that it can run on a cellphone, and it's so fast that it can display retouched images in real-time, so that the photographer can see the final version of the image while still framing the shot.

The same system can also speed up existing image-processing algorithms. In tests involving a new Google algorithm for producing high-dynamic-range images, which capture subtleties of color lost in standard digital images, the new system produced results that were visually indistinguishable from those of the algorithm in about one-tenth the time -- again, fast enough for real-time display.

The system is a machine-learning system, meaning that it learns to perform tasks by analyzing training data; in this case, for each new task it learned, it was trained on thousands of pairs of images, raw and retouched.

The work builds on an earlier project from the MIT researchers, in which a cellphone would send a low-resolution version of an image to a web server. The server would send back a "transform recipe" that could be used to retouch the high-resolution version of the image on the phone, reducing bandwidth consumption.

"Google heard about the work I'd done on the transform recipe," says Michaël Gharbi, an MIT graduate student in electrical engineering and computer science and first author on both papers. "They themselves did a follow-up on that, so we met and merged the two approaches. The idea was to do everything we were doing before but, instead of having to process everything on the cloud, to learn it. And the first goal of learning it was to speed it up."

Short cuts

In the new work, the bulk of the image processing is performed on a low-resolution image, which drastically reduces time and energy consumption. But this introduces a new difficulty, because the color values of the individual pixels in the high-res image have to be inferred from the much coarser output of the machine-learning system.

In the past, researchers have attempted to use machine learning to learn how to "upsample" a low-res image, or increase its resolution by guessing the values of the omitted pixels. During training, the input to the system is a low-res image, and the output is a high-res image. But this doesn't work well in practice; the low-res image just leaves out too much data.

Gharbi and his colleagues -- MIT professor of electrical engineering and computer science Frédo Durand and Jiawen Chen, Jon Barron, and Sam Hasinoff of Google -- address this problem with two clever tricks. The first is that the output of their machine-learning system is not an image; rather, it's a set of simple formulae for modifying the colors of image pixels. During training, the performance of the system is judged according to how well the output formulae, when applied to the original image, approximate the retouched version.

Taking bearings

The second trick is a technique for determining how to apply those formulae to individual pixels in the high-res image. The output of the researchers' system is a three-dimensional grid, 16 by 16 by 8. The 16-by-16 faces of the grid correspond to pixel locations in the source image; the eight layers stacked on top of them correspond to different pixel intensities. Each cell of the grid contains formulae that determine modifications of the color values of the source images.

That means that each cell of one of the grid's 16-by-16 faces has to stand in for thousands of pixels in the high-res image. But suppose that each set of formulae corresponds to a single location at the center of its cell. Then any given high-res pixel falls within a square defined by four sets of formulae.

Roughly speaking, the modification of that pixel's color value is a combination of the formulae at the square's corners, weighted according to distance. A similar weighting occurs in the third dimension of the grid, the one corresponding to pixel intensity.

The researchers trained their system on a data set created by Durand's group and Adobe Systems, the creators of Photoshop. The data set includes 5,000 images, each retouched by five different photographers. They also trained their system on thousands of pairs of images produced by the application of particular image-processing algorithms, such as the one for creating high-dynamic-range (HDR) images. The software for performing each modification takes up about as much space in memory as a single digital photo, so in principle, a cellphone could be equipped to process images in a range of styles.

Finally, the researchers compared their system's performance to that of a machine-learning system that processed images at full resolution rather than low resolution. During processing, the full-res version needed about 12 gigabytes of memory to execute its operations; the researchers' version needed about 100 megabytes, or one-hundredth as much. The full-resolution version of the HDR system took about 10 times as long to produce an image as the original algorithm, or 100 times as long as the researchers' system.

"This technology has the potential to be very useful for real-time image enhancement on mobile platforms," says Barron. "Using machine learning for computational photography is an exciting prospect but is limited by the severe computational and power constraints of mobile phones. This paper may provide us with a way to sidestep these issues and produce new, compelling, real-time photographic experiences without draining your battery or giving you a laggy viewfinder experience."

###

Additional background

PAPER: Deep bilateral learning for real-time image enhancement https://groups.csail.mit.edu/graphics/hdrnet/data/hdrnet.pdf

ARCHIVE: Streamlining mobile image processing http://news.mit.edu/2015/streamlining-mobile-image-processing-1113

ARCHIVE: Removing reflections from photos taken through windows http://news.mit.edu/2015/algorithm-removes-reflections-photos-0511

ARCHIVE: Spruce up your selfie http://news.mit.edu/2014/spruce-your-selfie

Media Contact

Abby Abazorius
abbya@mit.edu
617-253-2709

 @MIT

http://web.mit.edu/newsoffice 

Abby Abazorius | EurekAlert!

More articles from Information Technology:

nachricht NASA CubeSat to test miniaturized weather satellite technology
10.11.2017 | NASA/Goddard Space Flight Center

nachricht New approach uses light instead of robots to assemble electronic components
08.11.2017 | The Optical Society

All articles from Information Technology >>>

The most recent press releases about innovation >>>

Die letzten 5 Focus-News des innovations-reports im Überblick:

Im Focus: A “cosmic snake” reveals the structure of remote galaxies

The formation of stars in distant galaxies is still largely unexplored. For the first time, astron-omers at the University of Geneva have now been able to closely observe a star system six billion light-years away. In doing so, they are confirming earlier simulations made by the University of Zurich. One special effect is made possible by the multiple reflections of images that run through the cosmos like a snake.

Today, astronomers have a pretty accurate idea of how stars were formed in the recent cosmic past. But do these laws also apply to older galaxies? For around a...

Im Focus: Visual intelligence is not the same as IQ

Just because someone is smart and well-motivated doesn't mean he or she can learn the visual skills needed to excel at tasks like matching fingerprints, interpreting medical X-rays, keeping track of aircraft on radar displays or forensic face matching.

That is the implication of a new study which shows for the first time that there is a broad range of differences in people's visual ability and that these...

Im Focus: Novel Nano-CT device creates high-resolution 3D-X-rays of tiny velvet worm legs

Computer Tomography (CT) is a standard procedure in hospitals, but so far, the technology has not been suitable for imaging extremely small objects. In PNAS, a team from the Technical University of Munich (TUM) describes a Nano-CT device that creates three-dimensional x-ray images at resolutions up to 100 nanometers. The first test application: Together with colleagues from the University of Kassel and Helmholtz-Zentrum Geesthacht the researchers analyzed the locomotory system of a velvet worm.

During a CT analysis, the object under investigation is x-rayed and a detector measures the respective amount of radiation absorbed from various angles....

Im Focus: Researchers Develop Data Bus for Quantum Computer

The quantum world is fragile; error correction codes are needed to protect the information stored in a quantum object from the deteriorating effects of noise. Quantum physicists in Innsbruck have developed a protocol to pass quantum information between differently encoded building blocks of a future quantum computer, such as processors and memories. Scientists may use this protocol in the future to build a data bus for quantum computers. The researchers have published their work in the journal Nature Communications.

Future quantum computers will be able to solve problems where conventional computers fail today. We are still far away from any large-scale implementation,...

Im Focus: Wrinkles give heat a jolt in pillared graphene

Rice University researchers test 3-D carbon nanostructures' thermal transport abilities

Pillared graphene would transfer heat better if the theoretical material had a few asymmetric junctions that caused wrinkles, according to Rice University...

All Focus news of the innovation-report >>>

Anzeige

Anzeige

Event News

Ecology Across Borders: International conference brings together 1,500 ecologists

15.11.2017 | Event News

Road into laboratory: Users discuss biaxial fatigue-testing for car and truck wheel

15.11.2017 | Event News

#Berlin5GWeek: The right network for Industry 4.0

30.10.2017 | Event News

 
Latest News

NASA detects solar flare pulses at Sun and Earth

17.11.2017 | Physics and Astronomy

NIST scientists discover how to switch liver cancer cell growth from 2-D to 3-D structures

17.11.2017 | Health and Medicine

The importance of biodiversity in forests could increase due to climate change

17.11.2017 | Studies and Analyses

VideoLinks
B2B-VideoLinks
More VideoLinks >>>