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 Researchers illuminate the path to a new era of microelectronics
23.04.2018 | Boston University College of Engineering

nachricht Researchers achieve HD video streaming at 10,000 times lower power
20.04.2018 | University of Washington

All articles from Information Technology >>>

The most recent press releases about innovation >>>

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

Im Focus: Molecules Brilliantly Illuminated

Physicists at the Laboratory for Attosecond Physics, which is jointly run by Ludwig-Maximilians-Universität and the Max Planck Institute of Quantum Optics, have developed a high-power laser system that generates ultrashort pulses of light covering a large share of the mid-infrared spectrum. The researchers envisage a wide range of applications for the technology – in the early diagnosis of cancer, for instance.

Molecules are the building blocks of life. Like all other organisms, we are made of them. They control our biorhythm, and they can also reflect our state of...

Im Focus: Spider silk key to new bone-fixing composite

University of Connecticut researchers have created a biodegradable composite made of silk fibers that can be used to repair broken load-bearing bones without the complications sometimes presented by other materials.

Repairing major load-bearing bones such as those in the leg can be a long and uncomfortable process.

Im Focus: Writing and deleting magnets with lasers

Study published in the journal ACS Applied Materials & Interfaces is the outcome of an international effort that included teams from Dresden and Berlin in Germany, and the US.

Scientists at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) together with colleagues from the Helmholtz-Zentrum Berlin (HZB) and the University of Virginia...

Im Focus: Gamma-ray flashes from plasma filaments

Novel highly efficient and brilliant gamma-ray source: Based on model calculations, physicists of the Max PIanck Institute for Nuclear Physics in Heidelberg propose a novel method for an efficient high-brilliance gamma-ray source. A giant collimated gamma-ray pulse is generated from the interaction of a dense ultra-relativistic electron beam with a thin solid conductor. Energetic gamma-rays are copiously produced as the electron beam splits into filaments while propagating across the conductor. The resulting gamma-ray energy and flux enable novel experiments in nuclear and fundamental physics.

The typical wavelength of light interacting with an object of the microcosm scales with the size of this object. For atoms, this ranges from visible light to...

Im Focus: Basel researchers succeed in cultivating cartilage from stem cells

Stable joint cartilage can be produced from adult stem cells originating from bone marrow. This is made possible by inducing specific molecular processes occurring during embryonic cartilage formation, as researchers from the University and University Hospital of Basel report in the scientific journal PNAS.

Certain mesenchymal stem/stromal cells from the bone marrow of adults are considered extremely promising for skeletal tissue regeneration. These adult stem...

All Focus news of the innovation-report >>>

Anzeige

Anzeige

VideoLinks
Industry & Economy
Event News

Invitation to the upcoming "Current Topics in Bioinformatics: Big Data in Genomics and Medicine"

13.04.2018 | Event News

Unique scope of UV LED technologies and applications presented in Berlin: ICULTA-2018

12.04.2018 | Event News

IWOLIA: A conference bringing together German Industrie 4.0 and French Industrie du Futur

09.04.2018 | Event News

 
Latest News

Structured light and nanomaterials open new ways to tailor light at the nanoscale

23.04.2018 | Physics and Astronomy

On the shape of the 'petal' for the dissipation curve

23.04.2018 | Physics and Astronomy

Clean and Efficient – Fraunhofer ISE Presents Hydrogen Technologies at the HANNOVER MESSE 2018

23.04.2018 | Trade Fair News

VideoLinks
Science & Research
Overview of more VideoLinks >>>