Abstract:
A method determines a pixel value in a high dynamic range image from two images of different brightness by obtaining corresponding input pixel intensities from the two images, determining combination weights, and calculating the pixel value in the high dynamic range image as a weighted average of the input pixel intensities. Another method determines a pixel value in a high dynamic range image from more than two images by forming pairs of corresponding input pixel intensities, determining relative combination weights for the input pixels intensities for each pair, applying a normalization condition to determine absolute combination weights, and calculating the pixel value in the high dynamic range image as a weighted average of the input pixel intensities. Systems for generating high dynamic range image generation from two or more input images include a processor, a memory, a combination weight module, and a pixel value calculation module.
Abstract:
A system for brightness-sensitive automatic white balancing of an electronic color image includes a processor and a memory with (a) brightness-specific color-weighting maps each specifying illuminant-specific auto white balance parameters, (b) brightness range definitions respectively indicating applicability range of the brightness-specific color-weighting maps, and (c) instructions for white balancing the electronic color image according to scene brightness and based upon the brightness-specific color-weighting maps. A system for adaptive automatic white balancing of an electronic color image includes a processor and a memory having (a) a color-weighting map specifying a probability distribution of color ratios defining auto white balance parameters, the probability distribution composed of a plurality of illuminant-specific probability distributions respectively associated with a plurality of different spectral types of illuminant, and (b) instructions for processing one or more electronic color images to refine at least one of the plurality of illuminant-specific probability distributions.
Abstract:
A mobile computing device includes first, second and third cameras coupled to produce first, second and third camera video streams, respectively. The first camera is on a first side of the mobile computing device, and the second and third cameras are included in a stereo camera on a second side of the mobile computing device. A video processor is coupled to generate an output video stream including a first video layer generated from the first camera video stream. The video processor is further coupled to generate the output video stream to include second and third video layers from the second camera video stream in response to the second and the third camera video streams. The video processor is further coupled to overlay the first video layer between the second video layer and the third video layer in the output video stream.
Abstract:
A projector-camera system includes a projector coupled to back project a first image on a translucent diffusing screen. A camera is coupled to capture a second image from a back side of the translucent diffusing screen. The second image includes the first image back projected on the translucent diffusing screen and a shadow of a pointing device cast on a front side of the translucent diffusing screen. The pointing device is on the front side of the translucent diffusing screen and is in close proximity to the translucent diffusing screen. A processing block is coupled to the projector and the camera to generate a third image including the shadow of the pointing device. The processing block is further coupled to activate a command in a main computer coupled to the processing block in response to a relative position of the shadow of the pointing device in the third image.
Abstract:
An example method of multi-target automatic exposure and gain control based on pixel intensity distribution includes capturing a series of digital images with an image sensor. As the series of digital images are captured, exposure time and/or gain are adjusted to adjust a mean intensity value of the digital images until a target mean intensity value is reached. The method includes dynamically selecting the target mean intensity value from a plurality of target mean intensity values based on a relative number of pixels, in each captured digital image, that have an intensity value that falls outside a range of intensity values.
Abstract:
An image sensor includes an array of light sensitive elements and a filter array. Each filter element is in optical communication with a respective light sensitive element. The image sensor receives filtered light having a repeating pattern. Light sensitive elements in at least two successive rows alternately receive light having a first color and a second color, and light sensitive elements in common columns of the successive rows alternately receive light having the first color and the second color. Light sensitive elements in at least two additional successive rows alternately receive light having a third and a fourth color, and light sensitive elements in common columns of the additional successive rows alternately receive light having the third color and the fourth color. Output values of pairs of sampled light sensitive elements receiving light of a common color and from successive rows are combined to generate a down-sampled image.
Abstract:
A method of detecting light-emitting diode (LED) light starts with a control circuitry generating a shutter signal that is transmitted to a pixel array to control image acquisition by the pixel array and to establish a set exposure time. The readout circuitry may then read out the image data from the pixel array that includes reading out the image data from a plurality of successive and overlapped frames having the set exposure time. The set exposure time may be the same for each of the frames. The successive and overlapped frames may be interlaced frames. Other embodiments are also described.
Abstract:
An imaging system with on-chip phase-detection includes an image sensor with symmetric multi-pixel phase-difference detectors. Each symmetric multi-pixel phase-difference detector includes (a) a plurality of pixels forming an array and each having a respective color filter thereon, each color filter having a transmission spectrum and (b) a microlens at least partially above each of the plurality of pixels and having an optical axis intersecting the array. The array, by virtue of each transmission spectrum, has reflection symmetry with respect to both (a) a first plane that includes the optical axis and (b) a second plane that is orthogonal to the first plane. The imaging system includes a phase-detection row pair, which includes a plurality of symmetric multi-pixel phase-difference detectors in a pair of adjacent pixel rows and a pair, and an analogous phase-detection column pair.
Abstract:
An image sensor for on-chip phase detection includes a pixel array for capturing an image of a scene, wherein the pixel array has a plurality of horizontal phase-detection rows, each including phase-detection pixels for detecting horizontal change in the scene, and a plurality of vertical phase-detection columns, each including phase-detection pixels for detecting vertical change in the scene, and wherein each of the horizontal phase-detection rows intersects each of the vertical phase-detection columns. A phase-detection method includes generating a pair of horizontal line profiles using one of a plurality of phase-detection rows; generating a pair of vertical line profiles using one of a plurality of phase-detection columns intersecting with the one of a plurality of phase-detection rows; and determining phase shift associated with at least one arbitrarily oriented edge in a scene, based upon the pair of horizontal line profiles and the pair of vertical line profiles.
Abstract:
A method of generating a high resolution color image includes focusing a first image onto a monochrome image sensor having a P resolution and focusing a second image onto a color image sensor having a Q resolution, where Q