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Patent Application Titled "Noise Models for Image Processing" Published Online

August 26, 2014



By a News Reporter-Staff News Editor at Information Technology Newsweekly -- According to news reporting originating from Washington, D.C., by VerticalNews journalists, a patent application by the inventors Geiss, Ryan (Mountain View, CA); Zhou, Changyin (Mountain View, CA); Hasinoff, Samuel William (Mountain View, CA), filed on February 5, 2013, was made available online on August 14, 2014.

The assignee for this patent application is Google Inc.

Reporters obtained the following quote from the background information supplied by the inventors: "Generally, imaging may refer to capturing and representing the color and brightness characteristics of digital images (e.g., photographs and motion video). Low dynamic range (LDR) imaging may represent digital images with 8 or fewer bits for each color channel of a pixel. As a result, up to 256 levels of brightness may be supported. Currently, a wide range of video output devices (e.g., computer monitors, tablet and smartphone screens, televisions, etc.) support displaying LDR images.

"However, real-world scenes often exhibit a wider range of brightness than can be represented by LDR imaging. As an example scene with a wide brightness range, consider an individual standing in a dark room in front of a window. This scene may include both extremely bright regions (e.g., sunlit features outside the window) and extremely dark regions (e.g., the features in the room). Ideally, an image of this scene would include both the details in the bright regions and the details in the dark regions."

In addition to obtaining background information on this patent application, VerticalNews editors also obtained the inventors' summary information for this patent application: "A noise model of an image sensor and/or a camera device associated with the image sensor may be obtained. This noise model may be used to determine a noise deviation of the image sensor and/or camera device. For instance, the noise introduced by the image sensor and/or camera device may vary based on a captured pixel's brightness and color values. When merging two or more images, the noise model may be used to compare corresponding pixels of the images. If the corresponding pixels from one or more of the images differ by more than a noise deviation defined by the noise model, those pixels may be omitted when the images are merged. Alternatively or additionally, such a noise model may be used as part of image de-noising procedures, or for classifying pairs of aligned pixels in two images as in conflict or not.

"Accordingly, in a first example embodiment, a plurality of images of a scene may be obtained. The plurality of images may have been captured by an image sensor, and may include a first image and a second image. A particular gain may have been applied to the first image. An effective color temperature and a brightness of a first pixel in the first image may be determined. Based on the effective color temperature and the particular gain, a mapping between pixel characteristics and noise deviation of the image sensor may be selected. The pixel characteristics may include pixel brightness. The selected mapping may be used to map at least the brightness of the first pixel to a particular noise deviation. The brightness of the first pixel and the particular noise deviation may be compared to a brightness of a second pixel of the second image. The first and second images may have been aligned, at least to some extent. The comparison may be used to determine whether to merge the first pixel and the second pixel.

"A second example embodiment may include means for obtaining a plurality of images of a scene captured by an image sensor, wherein the plurality of images includes a first image and a second image, and wherein a particular gain has been applied to the first image. The second example embodiment may also include means for determining an effective color temperature and a brightness of a first pixel in the first image, and means for, based on the effective color temperature and the particular gain, selecting a mapping between pixel characteristics and noise deviation of the image sensor, wherein the pixel characteristics include pixel brightness. The second example embodiment may further include means for using the selected mapping to map at least the brightness of the first pixel to a particular noise deviation. The second example embodiment may additionally include means for comparing the brightness of the first pixel and the particular noise deviation to a brightness of a second pixel of the second image, and means for, based on the comparison, determining whether to merge the first pixel and the second pixel.

"A third example embodiment may include a non-transitory computer-readable storage medium, having stored thereon program instructions that, upon execution by a computing device, cause the computing device, and/or its peripherals, to perform operations in accordance with the first and/or second example embodiment.

"A fourth example embodiment may include a computing device, comprising at least a processor and data storage. The data storage may contain program instructions that, upon execution by the processor, cause the computing device operate in accordance with the first and/or second example embodiment.

"These as well as other aspects, advantages, and alternatives will become apparent to those of ordinary skill in the art by reading the following detailed description with reference where appropriate to the accompanying drawings. Further, it should be understood that the description provided in this summary section and elsewhere in this document is intended to illustrate the claimed subject matter by way of example and not by way of limitation.

BRIEF DESCRIPTION OF THE FIGURES

"FIG. 1 depicts front, right side, and rear views of a digital camera device, in accordance with an example embodiment.

"FIG. 2 depicts a block diagram of a computing device with image capture capability, in accordance with an example embodiment.

"FIG. 3 depicts a flow chart, in accordance with an example embodiment.

"FIG. 4 depicts image alignment, in accordance with an example embodiment.

"FIG. 5A depicts mapping pixel characteristics to a noise deviation, in accordance with an example embodiment.

"FIG. 5B also depicts mapping pixel characteristics to a noise deviation, in accordance with an example embodiment.

"FIG. 6 depicts using a noise deviation to determine whether to merge pixels, in accordance with an example embodiment.

"FIG. 7 is a flow chart, in accordance with an example embodiment."

For more information, see this patent application: Geiss, Ryan; Zhou, Changyin; Hasinoff, Samuel William. Noise Models for Image Processing. Filed February 5, 2013 and posted August 14, 2014. Patent URL: http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.html&r=5086&p=102&f=G&l=50&d=PG01&S1=20140807.PD.&OS=PD/20140807&RS=PD/20140807

Keywords for this news article include: Google Inc., Information Technology, Information and Data Storage.

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Source: Information Technology Newsweekly


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