Abstract: Systems and methods to selectively combine images are disclosed. In a particular embodiment, an apparatus includes a registration circuit configured to generate a set of motion vector data based on first image data corresponding to a first image and second image data corresponding to a second image. The apparatus includes a combination circuit to selectively combine the first image data and adjusted second image data that corresponds to the second image data adjusted according to the motion vector data. The apparatus further includes a control circuit to control the combination circuit to generate third image data.

Abstract: Image processing methods and systems are disclosed. In a particular embodiment, a method is disclosed that includes receiving image data. The image data includes color component data representing a location of a pixel in a color space. The method further includes performing a linear transformation of the location of the pixel in the color space when the location is identified as within a skin color region of the color space. The linear transformation is performed by mapping the location of the pixel at a first portion of the skin color region to a second portion of the skin color region based on a position of the pixel within the skin color region and based on the proximity of the position of the pixel to a boundary of the skin color region. The color space remains substantially continuous at the boundary of the skin color region after applying the linear transformation.

Abstract: Systems and methods of high dynamic range image combining are disclosed. In a particular embodiment, a device includes a global mapping module configured to generate first globally mapped luminance values within a region of an image, a local mapping module configured to generate second locally mapped luminance values within the region of the image, and a combination module configured to determine luminance values within a corresponding region of an output image using a weighted sum of the first globally mapped luminance values and the second locally mapped luminance values. A weight of the weighted sum is at least partially based on a luminance variation within the region of the image.

Abstract: In a particular embodiment, a method is disclosed that includes performing a first test using a first pixel value of a pixel to determine whether the pixel is outside a skin color region of a color space. The method includes, when the first test does not identify the pixel as outside the skin color region, performing a second test using a second pixel value of the pixel to determine whether the pixel is outside the skin color region of the color space. The method further includes, when the second test does not identify the pixel as outside the skin color region, performing a third test using a third pixel value of the pixel to determine whether the pixel is outside the skin color region of the color space.

Abstract: The imaging system converts raw data for an image to formatted data concurrently with compressing the formatted data for the image. The exemplary imaging system includes an image processor for generating blocks of formatted data from raw image data. The exemplary imaging system also includes an image compressor for compressing the blocks of formatted data. The compressor compresses one or more of the blocks while the image processor generates one or more blocks of formatted data.

Abstract: Methods and apparatus for processing a captured image of a medium, content on the medium, and a background surrounding the medium are provided. A captured image is processed by 1) improving the visibility of the content by recovering the original appearance of the medium and enhancing the appearance of the content image, 2) removing the background by determining the boundary of the medium, and/or 3) correcting geometric distortion in the content to improve readability. Any of the three processing steps may be used alone or any combination of the three processing steps may be used to process the image. The image processing may operate on the image after it is captured and stored to a memory and may be implemented on an image-capturing device that captures the image. In other embodiments, the image processing is implemented on another device that receives the image from the image-capturing device.

Abstract: Systems and methods of bad pixel cluster detection are disclosed. In a particular embodiment, a method includes determining a correlation value corresponding to a correlation coefficient between image data and at least one bad pixel cluster pattern, and detecting a bad pixel cluster corresponding to the at least one bad pixel cluster pattern based on the correlation value exceeding a threshold.

Abstract: A digital image system identifies defective pixels of a digital image sensor based on sensor values of pixels positioned in at least two dimensions on the digital image sensor. The exemplary imaging system includes a buffer for receiving sensor values that are each associated with a pixel in the digital image sensor and electronics for comparing the sensor value associated with a test pixel to the sensor values of pixels positioned in at least two dimensions on the digital image sensor. The electronics determines whether the test pixel is a defective pixel based on the comparison.

Abstract: Techniques for identifying and enhancing colors in a digital image associated with one or more target color shades. In an embodiment, the target color shades may include a shade of blue associated with the sky, a shade of green associated with outdoor foliage, or the color red. In an embodiment, the blue chroma (Cb) and red chroma (Cr) coordinates of a pixel are evaluated to determine whether to apply an enhancement factor. The enhancement factor may incorporate an exposure index (EI) auxiliary enhancement factor, a color temperature (D) auxiliary enhancement factor, and a luminance (Y) of each pixel. Further aspects for implementing the techniques in software and hardware are disclosed.

Abstract: This disclosure describes techniques for detecting a barcode within an image. An image processor may, for example, process an image to detect regions within the image that may be barcodes. The image processor may identify regions of the image that exhibit a high concentration of edges and a high concentration of pixels with low optical intensity co-instantaneously as potential barcodes. The image processor may identify the regions using a number of morphological operations. The image processor may then determine whether the identified regions are actually barcodes by verifying whether the region have unique barcode features. The barcode detection techniques described in this disclosure may be independent of barcode size, location and orientation within the image. Moreover, the use of morphological operations results in faster and more computationally efficient barcode detection, as well as lower computational complexity.

Abstract: This disclosure describes barcode scanning techniques for an image capture device. The image capture device may automatically detect a barcode within an image while the image capture device is operating in a non-barcode image capture mode, such a default image capture mode. In one aspect, the detection of the barcode within the image may be based on a combination of identified edges and low intensity regions within the image. The image capture device may configure, based on the detection of the barcode, one or more image capture properties associated with the image capture device to improve a quality at which the images are captured. The image capture device captures the image in accordance with the configured image capture properties. The techniques may effectively provide a universal and integrated front-end for producing improved quality images of barcodes without requiring significant interaction with a user via a complicated user interface.

Abstract: This disclosure describes techniques for rotating an encoded image, such as an image encoded according to a JPEG standard. In one example, a method for rotating an encoded image comprising reordering minimum coded units (MCUs) of the encoded image according to a specified rotation of the encoded image, rotating image data within the MCUs according to the specified rotation, and generating a rotated version of the encoded image comprising the reordered MCUs and the rotated image data within the MCUs.

Abstract: This disclosure describes an efficient architecture for an imaging device that supports image registration for still images and video coding of a video sequence. For image registration, the described architecture uses block-based comparisons of image blocks of a captured image relative to blocks of another reference image to support image registration on a block-by-block basis. For video coding, the described architecture uses block-based comparisons, e.g., to support for motion estimation and motion compensation. According to this disclosure, a common block comparison engine is used on a shared basis for both block-based image registration and block-based video coding. In this way, a hardware unit designed for block-based comparisons may be implemented so as to work in both the image registration process for still images and the video coding process for coding a video sequence.

Abstract: This disclosure describes image stabilization techniques for devices with image capture capabilities. An image capture device may capture two or more images and combine the image using the techniques described in this disclosure. In particular, the image capture device may compute motion vectors for a plurality of blocks of pixels of one of the images. In cases, the image capture device may also interpolate or extrapolate motion vectors for individual pixels or sub-blocks of pixels using the block motion vectors. The image capture device may then average the first and second images by averaging each of the pixels of the first image with pixels of the second image that correspond to a location indicated by the plurality of motion vectors. The techniques may be particularly effective in reducing blur in image information resulting from certain movements during image capture or use of certain image capture technologies.

Abstract: In general this disclosure describes techniques for configuring an image sensor of an image capture device based on motion within the scene of interest. In particular, the image capture device analyzes motion between two or more images of the same scene of interest and adjusts the configuration parameters, e.g., gain and/or exposure time, of the image sensor based on the amount of motion within the scene of interest. For example, the image capture device may configure the image sensor with a large gain and a short exposure time when the scene includes a relatively large amount of motion, thus reducing the blur caused by the large amount of motion. Conversely, the image capture device may configure the image sensor with a small gain and a long exposure time when the scene includes relatively little or no motion, thus reducing the noise caused by large gains.

Abstract: A two-dimensional (2D) mesh is applied over a distortion surface to approximate a lens roll-off distortion pattern. The process to apply the 2D mesh distributes a plurality of grid points among the distortion pattern and sub-samples the distortion pattern to derive corrected digital gains at each grid location. Non-grid pixels underlying grid blocks having a grid point at each corner are adjusted based on the approximation of the lens roll-off for the grid points of the grid block. In one example, bilinear interpolation is used. The techniques universally correct lens roll-off distortion irregardless of the distortion pattern shape or type. The technique may also correct for green channel imbalance.

Abstract: A device has a processing unit to implement a set of operations to use both luma and chroma information from a scene of an image to dynamically adjust exposure time and sensor gain. The processing unit collects bright near grey pixels and high chroma pixels in the scene. Based on the collected pixels, brightness of the near grey pixels is increased to a predetermined level without saturation. At the same time, the high chroma pixels are kept away from saturation.

Abstract: The rendering of 3D video images on a stereo-enabled display (e.g., stereoscopic or autostereoscopic display) is described. The process includes culling facets facing away from a viewer, defining foreground facets for Left and Right Views and common background facets, determining lighting for these facets, and performing screen mapping and scene rendering for one view (e.g., Right View) using computational results for facets of the other view (i.e., Left View). In one embodiment, visualization of images is provided on the stereo-enabled display of a low-power device, such as mobile phone, a computer, a video game platform, or a Personal Digital Assistant (PDA) device.

Abstract: Techniques for complexity-adaptive and automatic two-dimensional (2D) to three-dimensional (3D) image and video conversion which classifies a frame of a 2D input into one of a flat image class and a non-flat image class are described. The flat image class frame is directly converted into 3D stereo for display. The frame that is classified as a non-flat image class is further processed automatically and adaptively, based on complexity, to create a depth map estimate. Thereafter, the non-flat image class frame is converted into a 3D stereo image using the depth map estimate or an adjusted depth map. The adjusted depth map is processed based on the complexity.

Abstract: Automatic white balance of captured images can be performed based on a gray world assumption. One aspect relates to an apparatus comprising a collection module and a processor. The collection module is configured to accumulate (a) red/green and blue/green color ratio values of a plurality of pixels in a captured image for each cluster of a plurality of clusters and (b) a number of pixels having red/green and blue/green color ratios associated with each cluster, the clusters comprising daylight, fluorescent, incandescent, and a outdoor green zone. The processor is configured to determine which cluster has a highest accumulated number of pixels, and use the cluster with the highest accumulated number of pixels to perform white balancing for the captured image.