Abstract: Image tone adjustment using local tone curve computation may be utilized to adjust luminance ranges for images. Image tone adjustment using local tone curve computation may reduce the overall contrast of an image, while maintaining local contrast in smaller areas, such as in images capturing brightly lit scenes where the difference in intensity between brightest and darkest areas is large. A desired brightness representation of the image may be generated including target luminance values for corresponding blocks of the image. For each block, one or more tone adjustment values may be computed, that when jointly applied to the respective histograms for the block and neighboring blocks results in the luminance values that match corresponding target values. The tone adjustment values may be determined by solving an under-constrained optimization problem such that optimization constraints are minimized. The image may then be adjusted according to the computed tone adjustment values.

Abstract: Image tone adjustment using local tone curve computation may be utilized to adjust luminance ranges for images. Image tone adjustment using local tone curve computation may reduce the overall contrast of an image, while maintaining local contrast in smaller areas, such as in images capturing brightly lit scenes where the difference in intensity between brightest and darkest areas is large. A desired brightness representation of the image may be generated including target luminance values for corresponding blocks of the image. For each block, one or more tone adjustment values may be computed, that when jointly applied to the respective histograms for the block and neighboring blocks results in the luminance values that match corresponding target values. The tone adjustment values may be determined by solving an under-constrained optimization problem such that optimization constraints are minimized. The image may then be adjusted according to the computed tone adjustment values.

Abstract: The present disclosure generally relates to systems and methods for image data processing. In certain embodiments, an image processing pipeline may compute noise statistics associated with image data by receiving a frame of the image data having a plurality of pixels. The image processing pipeline may then identify a plurality of portions of the frame of the image data such that each portion of the plurality of portions has a flat surface. The image processing pipeline may then calculate a plurality of gradients for each portion of the plurality of portions, determine one or more dominant gradient orientations for each portion of the plurality of portions, and generate a histogram that represents a plurality of dominant gradient orientations that corresponds to the plurality of portions. After generating the histogram, the image processing pipeline may store the histogram, which may represent the noise statistics, in a memory.

Abstract: Semantically ranking content in a website (110) with a computerized ranking device (105) includes: parsing content from the website (110) into multiple autonomous content blocks (415-1 to 415-17) with the computerized ranking device (105) and assigning an importance ranking with said computerized ranking device (105) to each of the content blocks (415-1 to 415-17) based on a degree to which a substance of the content block (415-1 to 415-17) is relevant to one of a plurality of predefined categories.

Abstract: Disclosed herein are a system and a method that use a background model to determine and to segment target content from an image and replace them with different content to provide a composite image. The background model can be generated based on image data representing images of a predetermined area that does not include traversing content. The background model is compared to image data representing a set of captured images of the predetermined area. Based on the comparison, portions of an image that differs from the background model are determined as the traversing content. A target content model is used to determine the target content in the traversing content. The target content determined in the images is replaced with different content to provide a composite image.

Abstract: Systems and methods for processing raw image data are provided. One example of such a system may include memory to store image data in raw format from a digital imaging device and an image signal processor to process the image data. The image signal processor may include data conversion logic and a raw image processing pipeline. The data conversion logic may convert the image data into a signed format to preserve negative noise from the digital imaging device. The raw image processing pipeline may at least partly process the image data in the signed format. The raw image processing pipeline may also include, among other things, black level compensation logic, fixed pattern noise reduction logic, temporal filtering logic, defective pixel correction logic, spatial noise filtering logic, lens shading correction logic, and highlight recovery logic.

Abstract: A system and method of detecting separator lines in a web page may include determining coordinates of visible web elements on a web page, generating an edge image of the web page based on the coordinates of the web elements, filtering edges belonging to non-separator line elements within the edge image, detecting horizontal lines within the edge image, detecting vertical lines within the edge image, and filtering short lines within the edge image. A system for detecting separator lines in a web page may include a memory device, and a processor communicatively coupled to the memory, in which the processor determines coordinates of visible web elements on a web page, generates an edge image of the web page based on the coordinates of the web elements, filters edges belonging to non-separator line elements within the edge image, detects horizontal lines within the edge image, detects vertical lines within the edge image, and filters short lines within the edge image.

Abstract: The present disclosure generally relates to systems and methods for image data processing. In certain embodiments, an image processing pipeline may collect statistics associated with fixed pattern noise of image data by receiving a first frame of the image data comprising a plurality of pixels. The image processing pipeline may then determine a sum of a first plurality of pixel values that correspond to at least a first portion of the plurality of pixels such that each pixel in at least the first portion of the plurality of pixels is disposed along a first axis within the frame of the image data. After determining the sum of the first plurality of pixel values, the image processing pipeline may store the sum of the first plurality of pixel values in a memory such that the sum of the first plurality of pixel values represent the statistics.

Abstract: In one respect, provided are systems, methods and techniques in which local regions within an image are processed to provide fuzzy classification scores, which are calculated by determining changes in pixel values along a number of different directions. The resulting fuzzy classification scores are then used to detect or identify edge-containing or texture-containing regions, or to otherwise process the image regions differentially according to their fuzzy classification score. In another respect, provided are systems, methods and techniques for differential processing of different areas in an image. The differential processing in this case is based on calculated measures of local activity, which indicate features in corresponding local regions, and also based on calculated measures of local pixel-value variations, which indicate an amount of variation in pixel values across the corresponding local regions.

Abstract: A system and a method for near-duplicate image detection performed by a physical computing system includes applying a feature determining function to a number of images, a feature being defined by a geometric shape, comparing characteristics of said geometric shapes defining said features from at least two of said number of images, and characterizing said at least two of said number of images as a near-duplicate match if a predetermined percentage of said features of said at least two images match.

Abstract: A pattern includes a spatial configuration of color codes. Each color code is a unique configuration of colors selected from a number of basis colors. The color codes each include the same number of colors.

Abstract: A method for deblurring an image. A first image is captured. A second image is captured, wherein the second image is more blurred and more exposed than the first image. The second image is deblurred based on the first image.

Abstract: Systems and methods are provided for selectively performing image statistics processing based at least partly on whether a pixel has been clipped. In one example, an image signal processor may include statistics collection logic. The statistics collection logic may include statistics image processing logic and a statistics core. The statistics image processing logic may perform initial image processing on image pixels, at least occasionally causing some of the image pixels to become clipped. The statistics core may obtain image statistics from the image pixels. The statistics core may obtain at least one of the image statistics using only pixels that have not been clipped and excluding pixels that have been clipped.

Abstract: The present disclosure generally relates to systems and methods for image data processing. In certain embodiments, an image processing pipeline may detect and correct a defective pixel of image data acquired using an image sensor. The image processing pipeline may receive an input pixel of the image data acquired using the image sensor. The image processing pipeline may then identify a set of neighboring pixels having the same color component as the input pixel and remove two neighboring pixels from the set of neighboring pixels thereby generating a modified set of neighboring pixels. Here, the two neighboring pixels correspond to a maximum pixel value and a minimum pixel value of the set of neighboring pixels.

Abstract: The present disclosure generally relates to systems and methods for image data processing. In certain embodiments, an image processing pipeline may collect statistics associated with fixed pattern noise of image data by receiving a first frame of the image data comprising a plurality of pixels. The image processing pipeline may then determine a sum of a first plurality of pixel values that correspond to at least a first portion of the plurality of pixels such that each pixel in at least the first portion of the plurality of pixels is disposed along a first axis within the frame of the image data. After determining the sum of the first plurality of pixel values, the image processing pipeline may store the sum of the first plurality of pixel values in a memory such that the sum of the first plurality of pixel values represent the statistics.

Abstract: Systems and methods for processing raw image data are provided. One example of such a system may include memory to store image data in raw format from a digital imaging device and an image signal processor to process the image data. The image signal processor may include data conversion logic and a raw image processing pipeline. The data conversion logic may convert the image data into a signed format to preserve negative noise from the digital imaging device. The raw image processing pipeline may at least partly process the image data in the signed format. The raw image processing pipeline may also include, among other things, black level compensation logic, fixed pattern noise reduction logic, temporal filtering logic, defective pixel correction logic, spatial noise filtering logic, lens shading correction logic, and highlight recovery logic.

Abstract: The present disclosure generally relates to systems and methods for image data processing. In certain embodiments, an image processing pipeline may be configured to receive a frame of the image data having a plurality of pixels acquired using a digital image sensor. The image processing pipeline may then be configured to determine a first plurality of correction factors that may correct each pixel in the plurality of pixels for fixed pattern noise. The first plurality of correction factors may be determined based at least in part on fixed pattern noise statistics that correspond to the frame of the image data. After determining the first plurality of correction factors, the image processing pipeline may be configured to configured to apply the first plurality of correction factors to the plurality of pixels, thereby reducing the fixed pattern noise present in the plurality of pixels.

Abstract: Systems and methods for correcting green channel non-uniformity (GNU) are provided. In one example, GNU may be corrected using energies between the two green channels (Gb and Gr) during green interpolation processes for red and green pixels. Accordingly, the processes may be efficiently employed through implementation using demosaic logic hardware. In addition, the green values may be corrected based on low-pass-filtered values of the green pixels (Gb and Gr). Additionally, green post-processing may provide some defective pixel correction on interpolated greens by correcting artifacts generated through enhancement algorithms.

Abstract: Systems, methods, and devices for sharpening image data are provided. One example of an image signal processing system includes a YCC processing pipeline that includes luma sharpening logic. The luma sharpening logic may sharpen the luma component while avoiding sharpening some noise. Specifically, a multi-scale unsharp mask filter may obtain unsharp signals by filtering an input luma component, and sharp component determination logic may determine sharp signals representing differences between the unsharp signals and the luma component. Sharp lookup tables may “core” the sharp signals, which may prevent some noise from being sharpened. Output logic may determine a sharpened output luma signal by combining the sharp signals with, for example, luma component or one of the unsharp signals.

Abstract: The present disclosure generally relates to systems and methods for image data processing. In certain embodiments, an image processing pipeline may compute noise statistics associated with image data by receiving a frame of the image data having a plurality of pixels. The image processing pipeline may then identify a plurality of portions of the frame of the image data such that each portion of the plurality of portions has a flat surface. The image processing pipeline may then calculate a plurality of gradients for each portion of the plurality of portions, determine one or more dominant gradient orientations for each portion of the plurality of portions, and generate a histogram that represents a plurality of dominant gradient orientations that corresponds to the plurality of portions. After generating the histogram, the image processing pipeline may store the histogram, which may represent the noise statistics, in a memory.