Abstract: In one embodiment, a method is disclosed for performing a video processing. The method can extract one or more common video segments. The method can select a common summarization segment based on a first summarization score. The method can extract one or more individual video segments in which the number of segments included therein is not more than a third threshold value that is less than a second threshold value. The method can select an individual summarization segment based on a second summarization score. In addition, the method can integrate the common summarization segment and the individual summarization segment to create the summary video.

Abstract: Systems, methods, and computer readable media for performing color correction operations to address memory color artifacts in a manner suited for real-time operations. In general, techniques are disclosed for correcting memory color rendering artifacts in an image without performing color space conversions. In one implementations, hue-saturation-value (HSV) image correction values may be expressed solely in terms of an image's base red-green-blue (RGB) color space values. Once expressed in this manner, color correction may be applied to the image directly—without the need to convert the image's color space into and out of a working color space (e.g., an HSV color space). As no color space conversions are necessary, the disclosed techniques are well-suited to real-time operations.

Abstract: Methods, machine-readable media, and devices for face image prioritization based on face quality analysis are described herein. For example, one or more embodiments include detecting a facial image in an image that has been acquired by a camera that monitors a scene, passing the facial image through a number of quality analysis filters that include a number of quality analysis factors, wherein processing complexity associated with the number of quality analysis factors increases consecutively, and submitting the facial image to a facial recognition program upon a determination that the facial image has passed the number of quality analysis filters.

Abstract: An image processing apparatus capable of suppressing image quality deterioration in an output image region determined based on reference pixel regions including image ends. The image processing apparatus includes a coefficient selection value generator that generates a coefficient selection value according to a filter coefficient changeover control signal supplied from a region information controller. In accordance with the coefficient selection value, filter coefficient sets are respectively output from coefficient tables. One of the filter coefficient sets is selected by a selector in accordance with a coefficient set selection value supplied from a coefficient set selection unit. Using filter coefficients that constitute the selected filter coefficient set, an image processor performs arithmetic processing on image data on a per pixel of interest basis while referring to peripheral pixels.

Abstract: An image correction apparatus includes a correction factor calculating unit which calculates a correction factor such that a distance between a corrected value obtained by correcting, using the correction factor, a luminance value of a first pixel on the image corresponding to a sensor element with a first filter in an image sensor included in an image capturing unit which generates the image, the first filter having a first transmittance characteristic and a luminance value of a second pixel on the image corresponding to a sensor element with a second filter in the image sensor, the second filter having a second transmittance characteristic, becomes smaller than a distance between the luminance value of the first pixel and the luminance value of the second pixel, and a correcting unit which generates a corrected image by correcting the luminance value of the first pixel using the correction factor.

Abstract: An aspect of the invention includes a system and method for determining a block size and offset for a block artifact. A content is identified. The content can be an image, a frame of video, or any other appropriate content. Edge differences are calculated in each dimension of the content based on the pixel values. The edge differences are filtered. From the filtered edge differences, block attributes, such as block size and offset, can be determined.

Abstract: Provided is an image processing apparatus including: a frequency value calculation section that allocates each pixel of an input image to any of respective partial regions obtained by dividing the entirety of a possible range of a luminance value into units in a luminance direction on the basis of the luminance value thereof, and allocates one pixel of the input image to partial regions when calculating frequency values representing the number of pixels allocated to the partial regions with respect to the respective partial regions, to update the frequency values of the partial regions; a characteristic value calculation section that calculates a characteristic value representing a characteristic of the partial region; and a weighted product-sum section that performs edge-preserving smoothing on the input image by weighting and averaging the characteristic values in accordance with a distance in the luminance direction, using the calculated frequency value and the calculated characteristic value.

Abstract: An image processing apparatus for performing recovery processing of correcting degradation of an image due to an aberration of an optical imaging system with respect to image data obtained by sensing an object image entering through the optical imaging system using an image sensor, comprises: a selection unit configured to select a filter to be used for recovery processing in accordance with an image sensing condition at the time of the image sensing; an image processing unit configured to perform recovery processing for the image data by using the filter selected by the selection unit; and a correction unit configured to determine a correction value based on a difference between the value of a pixel before and after recovery processing by the image processing unit, and correcting the pixel value before the recovery processing by the image processing unit using the determined correction value.

Abstract: A parametric loop filter uses a set of fixed filters to remove or reduce noise and artifacts introduced during video coding. The filters are pre-trained offline and hardwired into encoder and decoder, instead of online trained Wiener filters. The filters are able to be specified using one or more parameters including: direction, bandwidth along the direction (bw//) and bandwidth perpendicular to the direction (bw?). The filter to be used is able to be derived from local image characteristics or predicted from neighboring blocks. The parametric loop filter utilizes much less computation, delay and memory access at the encoder. Fixed coefficients allow fast implementation of filtering at the decoder. A parametric loop filter is able to be combined with online training to further improve performance, by allowing one or more fixed filters to be replaced with online trained Wiener filters.

Abstract: A Digital Image Stabilization method including selecting a principal transform representing a stationary/background object in the scene of a video frame, based on scoring each of a plurality of transforms of tile motion vector (Tile MV) groups and of feature point motion vector (FP MV) groups, and excluding large moving objects based on the history of the stationary (background) group and the history of each of the plurality motion vector groups.

Abstract: A system, method, and computer program product are provided for reducing noise in an image using depth-based on sweeping over image samples. In use, each noisy pixel of an image having noise is identified. Additionally, for each noisy pixel, at least one sample included in each of a plurality of neighboring pixels to the noisy pixel is identified. Furthermore, the samples are swept over at least partially in a depth-based order to identify a value for the noisy pixel that reduces the noise.

Abstract: An image processing device includes: a first band limiting unit that reduces noise included in an input image; and a restoring unit, wherein the restoring unit has a difference calculating unit that calculates a difference between the input image and a noise-reduced image, a predicted noise obtaining unit that obtains a predicted noise amount to be included in the input image, a correction signal generating unit that generates a correction signal for correcting the noise-reduced image and controlling an absolute value of the difference between the input image and the noise-reduced image within a range of the predicted noise amount, and an image synthesizing unit that corrects the noise-reduced image based on the correction signal.

Abstract: Accurately estimating the image quality of video is still difficult, especially when there is no reference video available. A method for determining a quality measure for a video image comprises selecting a measuring point, such as a MB, determining a context area around the measuring point, calculating a variance of pixel values in the context area, calculating a variance of pixel values in the measuring point, calculating a relation between the two variances of pixel values, and averaging said relations for a plurality of measuring points, wherein a quality measure for a video image is obtained.

Abstract: A direction-adaptive image upsampling system and method using double interpolation is disclosed. Each of double interpolation (DI) units with different interpolation functions receives an input image and generates a corresponding double-interpolated image and an associated double-interpolated difference. A decision unit receives the double-interpolated differences from the DI units for deciding the DI unit that has an optimal interpolation around a pixel under process. An adaptive selector selects the double-interpolated image associated with the DI unit having the optimal interpolation as an output image.

Abstract: Reference-free compensated imaging makes an estimation of the Fourier phase of a series of images of a target. The Fourier magnitude of the series of images is obtained by dividing the power spectral density of the series of images by an estimate of the power spectral density of atmospheric turbulence from a series of scene based wave front sensor (SBWFS) measurements of the target. A high-resolution image of the target is recovered from the Fourier phase and the Fourier magnitude.

Abstract: An image correction apparatus includes a read image acquiring unit that acquires read image data generated by an imaging device that moves in a sub-scanning direction with respect to a reading medium irradiated with an environmental light to read the reading medium; a selecting unit that selects a correction filter for correcting a pixel of interest in the acquired read image data based on brightness or luminance component of a pixel in a determination filter including the pixel of interest; and a correction unit that corrects tone data of the pixel of interest based on the correction filter selected by the selecting unit. The determination filter includes a plurality of determination filters in which a number of pixels to be used in the sub-scanning direction differs.

Abstract: A prediction error (eq[x,y]) is added to a predicted frame ({circumflex over (f)}[x,y]) or a predicted block for receiving a decoded frame (gq[x,y]) or a decoded block to be further used in a prediction loop by an encoder or to be sent to the output of a decoder. The reference frame (gq[x,y]) or the reference block includes a useful signal part and a noise signal part. The reference frame (gq[x,y]) or reference block pass through a dedicated noise reducing filter to reduce or eliminate the noise signal part of the reference frame (gq[x,y]) or reference block.

Abstract: A video image capture component includes a light source operable in a first spectrum, a first image detector operable in the first spectrum, a second light source operable in a second spectrum, and a second image detector operable in the second spectrum. A filtering component generates a combination image by filtering a first image obtained by the first image detector with a high-contrast filter, resulting in a high-contrast image, and masking a second image obtained by the second image detector using the high-contrast image. A compositing component creates a composite image from the combination image and a selected image. A display component displays the composite image. Alternative systems and methods for creating a combination image include techniques involving thermal imaging, laser detection, and narrow band frequency detection.

Abstract: A hardware-implemented function evaluator performs mathematical calculations at high speeds to generate data values in place of an LUT. The disclosed embodiments can generate a small number of output values from a large number of input values. The calculations can use functions that are monotonically increasing such as, for example, square root, power curves, and trigonometric functions.

Abstract: Systems and methods are provided for upscaling a digital image. A digital image to be upscaled is accessed, where the digital image comprises a plurality of pixel values. A first half pixel value is computed for a first point in the digital image based on a plurality of pixel values of the digital image surrounding the first point and an activity level. A second half pixel value is computed for a second point in the digital image, and an interpolated pixel of an upscaled version of the digital image is determined using a plurality of the pixel values, the first half pixel value, and the second half pixel value.

Abstract: One or more systems and methods for edge-adaptive and recursive non-linear filtering of ringing effect on image or video data are disclosed in accordance with various embodiments of the invention. In one embodiment of the invention, a method for edge-adaptive and recursive non-linear filtering of ringing effect first involves detecting all edges within a process area of an image, including a direction of an edge slope and a value of an edge signal level. Then, if the process area is determined to be a “non-busy” area based on a “busyness” measure relative to empirically-defined threshold values, then an edge influence function subsequently determines whether to apply a de-ringing filter to a current pixel within the process area or not. Preferably, the de-ringing filter is edge-adaptive, non-linear, and recursive. The de-ringing filter can adjust the current pixel multiple times based on filter angles, adjacent pixels, and pixel transition levels.

Abstract: There is provided an output device including an inputting unit, an extracting unit, a calculating unit, an adjusting unit and an outputting unit. The inputting unit is configured to input an image file representing a motion image. The extracting unit is configured to extract, from the image file, a first frame image and at least one neighboring frame image that is chronologically continuous with the first frame image. The calculating unit is configured to calculate a total adjustment parameter for adjusting the first frame image based on the first frame image and the at least one neighboring frame image. The adjusting unit is configured to adjust the first frame image using the total adjustment parameter to generate a first adjusted output image. The outputting unit is configured to output the first adjusted output image.

Abstract: Various techniques are disclosed for reducing spatial and temporal noise in captured images. In one example, temporal noise may be filtered while still retaining temporal responsivity in filtered images to allow low contrast temporal events to be captured. Spatial and temporal noise filters may be selectively weighted to more strongly favor filtering using whichever one of the filters is least likely to cause a loss of signal fidelity in actual scene content. Other techniques are disclosed for determining various parameters of imaging systems having image lag. For example, a mean-variance characterization and a noise equivalent irradiance characterization may be performed to determine parameters of the imaging systems. Results of such characterizations may be used to determine the actual performance of the imaging systems without the effects of image lag.

Abstract: Method and apparatus for performing visual effects rendering on an image are described. User specifications are processed for at least one visual effect for the image. Scene analysis information associated with the image may be accessed, wherein the scene analysis information is based on the user specifications and indicates at least one pixel of the image for coupling with the at least one visual effect. The at least one pixel is accentuated using depth map information, wherein dimensions for the at least one pixel are determined in response to corresponding depth values of the at least one pixel. An output image is generated having at least one visual effect.

Abstract: Systems and methods for image data fusion include providing first and second sets of image data corresponding to an imaged first and second scene respectively. The scenes at least partially overlap in an overlap region, defining a first collection of overlap image data as part of the first set of image data, and a second collection of overlap image data as part of the second set of image data. The second collection of overlap image data is represented as a plurality of image data subsets such that each of the subsets is based on at least one characteristic of the second collection, and each subset spans the overlap region. A fused set of image data is produced by an image processor, by modifying the first collection of overlap image data based on at least a selected one of, but less than all of, the image data subsets.

Abstract: A method for performing fast detail-preserving filtering of an input digital image includes, for each pixel in the image, calculating the difference between the selected pixel and each of its four neighboring pixels located above, left, right, and below the selected pixel, calculating a scaled weighted sum of differences between the actual pixel and its four neighbors, where for each neighboring pixel the weight is an edge-sensing function having a data-adaptive scaling parameter function, and adding the weighted sum of differences to the value of the selected pixel.

Abstract: To prevent an increase in a data storage space required for image restoration processing, a non-transitory computer-readable storage medium according to the present invention stores a process for causing an information processing apparatus to execute a method including acquiring identification information for identifying an optical transfer characteristic for restoring an image, acquiring the optical transfer characteristic identified based on the identification information from a storage unit storing a first optical transfer characteristic commonly usable for first and second captured images captured under different imaging conditions and a second optical transfer characteristic usable for a third captured image, and generating a restored image with use of the acquired optical transfer characteristic.

Abstract: Systems and methods for texture processing are presented. In one embodiment a texture method includes creating a sparse texture residency translation map; performing a probe process utilizing the sparse texture residency translation map information to return a finest LOD that contains the texels for a texture lookup operation; and performing the texture lookup operation utilizing the finest LOD. In one exemplary implementation, the finest LOD is utilized as a minimum LOD clamp during the texture lookup operation. A finest LOD number indicates a minimum resident LOD and a sparse texture residency translation map includes one finest LOD number per tile of a sparse texture. The sparse texture residency translation can indicate a minimum resident LOD.

Abstract: A depth noise filtering method and apparatus is provided. The depth noise filtering method may perform spatial filtering or temporal filtering according to depth information. In order to perform spatial filtering, the depth noise filtering method may determine a characteristic of a spatial filter based on depth information. Also, in order to perform temporal filtering, the depth noise filtering method may determine a number of reference frames based on depth information. The depth noise filtering method may adaptively remove depth noise according to depth information and thereby enhance a noise filtering performance.

Abstract: A method of processing a digital image. The method comprises: populating (100) a bilateral grid (10) comprising a plurality of cells (20), based on information obtained from the image, each cell comprising at least one value; then cumulatively integrating (110) the at least one value over the bilateral grid to form an integrated grid comprising integrated values; generating (120) a modified bilateral grid, comprising computing the sum of the at least one value over a predetermined rectangular box of cells of the bilateral grid, by using the integrated values corresponding to the corners of the box; and slicing (130) the modified bilateral grid to generate an output image.

Abstract: A system for enhancing an input image including receiving an input image and filtering the input image with a plurality of non-linear smoothing filters providing a respective plurality of filtered outputs. The system processes a plurality of the filtered outputs with respect to at least one of another of the filtered outputs and the input image to determine a plurality of detail layers. The system filters the plurality of detail layers with a plurality of non-linear smoothing filters providing a respective plurality of smoothed layers. The system adjusts the plurality of smoothed layers in such a manner that regions closer to an edge are enhanced to a lesser extent than regions farther from an edge and combining the adjusted the smoothed layers to provide an enhanced output image.

Abstract: Systems and methods for generating a composite image from a plurality of source images using a scene dependent multi-band blending operation are provided. The multi-band blending operation implements a filtering operation to reduce blending between objects or surfaces that have natural color and/or brightness differences. More particularly, the typical space invariant upsampling that occurs during multi-band blending can be replaced by a scene dependent filtering operation during upsampling that omits or reduces contributions from pixels associated with different objects in a scene during the multi-band blending process. The scene dependent filtering can be based on scene dependent data, such as height data or slope data, which can be used to identify different objects in a scene.

Abstract: A method and apparatus for detecting and compensating for a backlight frame are provided. The method includes receiving data regarding a current frame comprising pixels that are expressed by a luminance component and one or more chrominance components; and determining whether the current frame is a backlight frame based on values of the one or more chrominance components of the pixels.

Abstract: A system and method for generating a fused image is provided. The system comprises processing circuitry configured to receive a plurality of images, filter each received image using an edge preserving filter, compute a weight for each received image based on the corresponding filtered image and the received image and generate a fused image based on the weights of each received image. The system further comprises a memory device configured to store the fused image.

Abstract: A method for predicting whether a test image (318) is sharp or blurred includes the steps of: providing a sharpness classifier (316) that is trained to discriminate between sharp and blurred images; computing a set of sharpness features (322) for the test image (318) by (i) generating a high pass image (404) from the test image (318), (ii) generating a band pass image (406) from the test image (318), (iii) identifying textured regions (408) in the high pass image, (iv) identifying texture regions (410) in the band pass image, and (v) evaluating the identified textured regions in the high pass image and the band pass image to compute the set of test sharpness features (412); and evaluating the sharpness features using the sharpness classifier (324) to estimate if the test image (318) is sharp or blurry (20).

Abstract: A symmetric filter arithmetic apparatus includes a first data shuffling unit which reads a first data string that is a plurality of consecutive pieces of data from a register file and extract, from the first data string, a left-side data string that is a plurality of consecutive pieces of data to be multiplied by a left-side filter coefficient that is a filter coefficient on a left side of a center of the coefficients, and a second data shuffling unit which reads a second data string that is a plurality of consecutive pieces of data from the register file and extract, from the second data string, a right-side data string that is a plurality of consecutive pieces of data to be multiplied by a right-side filter coefficient that is a filter coefficient on a right side of the center and is the same value as the left-side filter coefficient.

Abstract: An image comprising varying illumination is selected. Patches of pixels from among the plurality of pixels with the image are identified. Similarities between pairs of patches of pixels based on pixel intensities associated with the pairs of patches of pixels are calculated. Illumination values for the plurality of pixels within the image based on the calculated similarities between the pairs of patches of pixels is calculated. The illumination variation from the image is removed based on the calculated illumination values for the plurality of pixels within the image.

Abstract: An image processing method includes the steps of obtaining an image generated by an image pickup system, and performing correction processing for the image by utilizing an image restoration filter generated or selected based on an optical transfer function of the image pickup system. The image restoration filter is a filter configured to reduce a phase degradation component of the image.

Abstract: An apparatus usable in an image encoding and/or decoding system includes a segmentation unit to convert a first image of a first resolution into a second image of a second resolution, to segment the second image of the second resolution with one or more blocks of a binary mask layer having a foreground and a background, and to convert the segmented second image into a third image of a third resolution as a segmented image.

Abstract: A method and system for mapping colors of an image. The method including the steps of: segmenting an image into a plurality of regions, so that pixels in each of the plurality of regions have a characteristic that meets a predetermined similarity; determining an initial contrast between adjacent regions in the plurality of regions; and transforming an initial color of each of the plurality of regions into a target color; where a target contrast between the adjacent regions in the plurality of regions is equal to or greater than the initial contrast or a difference between the target contrast and corresponding initial contrast is less than a predetermined threshold; and where at least one of the steps is carried out by a computer device.

Abstract: An apparatus is described. The apparatus includes a digital camera that is capable of shooting at least two images at different effective lens focal lengths. The camera is also capable of high pass filtering (HP) said at least two images, estimating respective spectral densities (SD) of said at least two filtered images and then low pass filtering (LP) the respective estimates of said at least two filtered images prior to combining said at least two images into a single image to produce one or more optical effects.

Abstract: In a first exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the steps of providing an image file depicting an image, in a computer memory, organizing information relevant to the image according to a smooth-illumination constraint enforcing a Laplacian filter response such that ?2ƒ(x, y)=0 and utilizing the smooth-illumination constraint to generate an intrinsic image from the image.

Abstract: A method includes receiving k-space data corresponding to magnetic resonance data for a subject and selecting a template for analysis. In addition, the method includes generating an image using the k-space data and using the template.

Abstract: System and method for enhancing at least one atmospheric parameter of interest provided in remotely-sensed data by detecting and suppressing false alarm data, including computer code to receive measurement data and background including false alarms, computer code to conduct detection tests for the atmospheric parameter, computer code to compute the strength of the tests, and computer code to weight the measurement data based on the strengths and enhance the measurement data based on the weighted data.

Abstract: A computer-implemented method for resizing an image using a seam carving algorithm. The method may include measuring energy levels of pixels in an original image to derive an original energy map; applying a filter to an original energy map to derive a first energy map having a scale less than the original energy map; iteratively applying the filter N times, starting with the first energy map, to an energy map from an immediately preceding iteration; upsampling each of the energy maps to a resolution that matches the original energy map; combining the upsampled energy maps with the original energy map to form a composite image; identifying a seam by finding a path in the composite image having lowest energy quantities from one end of the composite image to an opposing end of the composite image; and selectively deleting the identified seam from the original image, thereby yielding a resized image.

Abstract: An image processing apparatus includes: a normal interpolated image generation unit to generate an image that is interpolated between a plurality of original images reproduced along time series, the image being a normal interpolated image, based on each of the plurality of original images; a high-frequency area extraction unit to extract a high-frequency area having a spatial frequency higher than a predetermined value in each of the plurality of original images; a high-frequency area interpolated image generation unit to generate an image that is interpolated between the plurality of original images, the image being a high-frequency area interpolated image, based on a change in position of the high-frequency area along with an elapse of time on the time series and on each of the plurality of original images; and a combination unit to execute combining processing to combine the normal interpolated image and the high-frequency area interpolated image.

Abstract: Encoding efficiency of image prediction encoding can be improved by performing horizontal packing as horizontal processing to manipulate a horizontal-direction array of pixels of each of a first thinned-out image and a second thinned-out image arrayed in checkerboard fashion, obtained by thinning out the pixels of each of a first image and a second image different from the first image every other line in an oblique direction, wherein pixels of first and second thinned-out images are packed in the horizontal direction. A combined image can be generated, which is combined by adjacently arraying the post-horizontal processing first and second thinned-out images after horizontal processing, as an image to serve as the object of prediction encoding. Aspects of this disclosure can be applied to a case of performing prediction encoding on a first and second image, such as a left and right images making up a 3D image.

Abstract: An image processing apparatus capable of suppressing image quality deterioration in an output image region determined based on reference pixel regions including image ends. The image processing apparatus includes a coefficient selection value generator that generates a coefficient selection value according to a filter coefficient changeover control signal supplied from a region information controller. In accordance with the coefficient selection value, filter coefficient sets are respectively output from coefficient tables. One of the filter coefficient sets is selected by a selector in accordance with a coefficient set selection value supplied from a coefficient set selection unit. Using filter coefficients that constitute the selected filter coefficient set, an image processor performs arithmetic processing on image data on a per pixel of interest basis while referring to peripheral pixels.

Abstract: Disclosed herein is a method for selectively filtering and encoding a video signal having at least one frame including a matrix of pixels. The method includes selecting a set of pixels within the frame, determining an initial performance measurement for the selected set of pixels, provisionally applying each filter to the set of pixels, determining a second performance measurement for each filter provisionally applied to the selected set of pixels, and applying the at least one filter to the selected set of pixels to generate a reconstruction of the frame if the at least one filter is determined to have a second performance measurement that is better than the initial performance measurement.

Abstract: A method for producing an image having a high signal-to-noise ratio (SNR) is provided. An image to be enhanced is provided, the provided image including a previously reconstructed image or an image reconstructed from acquired image data. A prior image is produced from the provided image, for example, by filtering the provided image such that noise from the provided image is substantially suppressed in the prior image. Synthesized image data is produced by performing a forward projection of the provided image. A sparsified image is produced by subtracting the prior image and the provided image. A target image having a higher SNR than the provided image is reconstructed using the sparsified image, the provided image, and the synthesized image data. The provided image may be, for example, a medical image produced by an x-ray imaging system, including computed tomography and C-arm systems; a magnetic resonance imaging system; and the like.