Abstract: An electronic device, method, and computer readable medium for multi-frame image processing using semantic saliency are provided. The electronic device includes a camera, a display, and a processor. The processor is coupled to the camera and the display. The processor receives a plurality of frames captured by the camera during a capture event; identifies a salient region in each of the plurality of frames; determines a reference frame from the plurality of frames based on the identified salient regions; fuses non-reference frames with the determined reference frame into a completed image output.

Abstract: Traditionally, providing parallel processing within a multi-core system has been very difficult. Here, however, a system is provided where serial source code is automatically converted into parallel source code, and a processing cluster is reconfigured “on the fly” to accommodate the parallelized code based on an allocation of memory and compute resources. Thus, the processing cluster and its corresponding system programming tool provide a system that can perform parallel processing from a serial program that is transparent to a user. Generally, a control node connected to the address and data leads of a host processor uses messages to control the processing of data in a processing cluster. The cluster includes nodes of parallel processors, shared function memory, a global load/store, and hardware accelerators all connected to the control node by message busses. A crossbar data interconnect routes data to the cluster circuits separate from the message busses.

Abstract: A method includes identifying feature points in an image in images generated of a scene by a camera and identifying locations of the identified feature points in remaining images in the images. The method also includes selecting a group of the identified feature points indicative of relative motion of the camera between image captures and aligning a set of the images using the selected group of feature points. The method may further include selecting a reference image from the set of aligned images, weighting other images the set, and combining the reference image with the weighted images. Weighting of the other images may include, for each other image in the set, comparing the other image and the reference image to identify one or more moving objects in the other image and applying a weight to pixel locations in the other image.

Abstract: A mobile device includes an embedded digital camera that is configured to capture a burst of N images. The mobile device includes processing circuitry comprising a registration module configured to, for each image within the burst of images: analyze an amount of warp of the image and generate a set of affine matrices indicating the amount of warp of the image. The processing circuitry includes a High Fidelity Interpolation block configured to, for each image within the burst of images: perform affine transformation using the set of affine matrices associated with the image, apply an aliasing-retaining interpolation filter, and implement rotation transformation and sub-pixel shifts, yielding an interpolated image. The processing circuitry includes a blending module configured to receive the N interpolated images and blend the N interpolated images into a single-blended image having a user-selected digital zoom ratio.

Abstract: A mobile device includes an embedded digital camera that is configured to capture a burst of N images. The mobile device includes processing circuitry comprising a registration module configured to, for each image within the burst of images: analyze an amount of warp of the image and generate a set of affine matrices indicating the amount of warp of the image. The processing circuitry includes a High Fidelity Interpolation block configured to, for each image within the burst of images: perform affine transformation using the set of affine matrices associated with the image, apply an aliasing-retaining interpolation filter, and implement rotation transformation and sub-pixel shifts, yielding an interpolated image. The processing circuitry includes a blending module configured to receive the N interpolated images and blend the N interpolated images into a single-blended image having a user-selected digital zoom ratio.

Abstract: A method and apparatus for aligning and combining images. The method includes identifying feature points in an image in images generated of a scene by a camera and identifying locations of the identified feature points in remaining images in the images. The method also includes selecting a group of the identified feature points indicative of relative motion of the camera between image captures and aligning a set of the images using the selected group of feature points. The method may further include selecting a reference image from the set of aligned images, weighting other images the set, and combining the reference image with the weighted images. Weighting of the other images may include, for each other image in the set, comparing the other image and the reference image to identify one or more moving objects in the other image and applying a weight to pixel locations in the other image.

Abstract: Traditionally, providing parallel processing within a multi-core system has been very difficult. Here, however, a system in provided where serial source code is automatically converted into parallel source code, and a processing cluster is reconfigured “on the fly” to accommodate the parallelized code based on an allocation of memory and compute resources. Thus, the processing cluster and its corresponding system programming tool provide a system that can perform parallel processing from a serial program that is transparent to a user.

Abstract: An activity-based system for, and method of, reducing Gr-Gb gain imbalance and a digital camera incorporating the system or the method. In one embodiment, the system includes: (1) a sensor configured to provide a input Bayer pattern array containing amplitudes corresponding to Gr and Gb cells and (2) a processor coupled to the sensor and configured to (2a) compute for at least some of the Gr and Gb cells: activity measures for pluralities of adjacent, same-type cells, green precompensation factors based on the activity measures, averages for the pluralities of adjacent, same-type cells and averages for pluralities of adjacent, opposite-type cells and (2b) use the green precompensation factors, the averages for the pluralities of adjacent, same-type cells and the averages for the pluralities of adjacent, opposite-type cells to form an output Bayer pattern in which the Gr-Gb gain imbalance is reduced.

Abstract: A method and apparatus for focusing an image in an image-capturing device utilizing at least one face in the image. The method includes detecting at least one face in the image, determining the size of the detected at least one face, determining the distance of the at least one face from the image-capturing device, wherein the determination utilizes the size of the detected at least one face, and focusing an image according to the determined distance of the detected at least one face.

Abstract: An activity-based system for, and method of, reducing Gr-Gb gain imbalance and a digital camera incorporating the system or the method. In one embodiment, the system includes: (1) a sensor configured to provide a input Bayer pattern array containing amplitudes corresponding to Gr and Gb cells and (2) a processor coupled to the sensor and configured to (2a) compute for at least some of the Gr and Gb cells: activity measures for pluralities of adjacent, same-type cells, green precompensation factors based on the activity measures, averages for the pluralities of adjacent, same-type cells and averages for pluralities of adjacent, opposite-type cells and (2b) use the green precompensation factors, the averages for the pluralities of adjacent, same-type cells and the averages for the pluralities of adjacent, opposite-type cells to form an output Bayer pattern in which the Gr-Gb gain imbalance is reduced.