Abstract: An image signal processor includes a gradation detection module configured to receive a pixel signal from an external device and a correction module configured to receive the pixel signal. The correction module is connected to the gradation detection module. The gradation detection module is configured to estimate a gradient of the pixel signal, correct the pixel signal based on the gradient to generate a corrected pixel signal, calculate a first variance value based on the pixel signal, calculate a second variance value based on the corrected pixel signal, calculate a gradation probability value based on a comparison result between the first variance value and the second variance value, and generate a gradation map as information about the gradation probability value of the pixel signal. The correction module is configured to correct the pixel signal based on the gradation map.

Abstract: An image processing apparatus includes a parameter control section adapted to acquire, at a predetermined rate, motion information of an imaging apparatus that captures a video and control an exposure time on the basis of the motion information, and an output section adapted to output data obtained by performing a predetermined process on an image captured by the imaging apparatus with the exposure time.

Abstract: Methods and apparatus for processing images captured by cameras for use in stereo depth determinations are described and/or for using depth information generated by processing such images is described. Images are captured using a reference camera and at least one additional camera. Filtering is implemented as part of a patch matching process to reduce the risk of erroneous matches, e.g., due to image blur in one image but not another. The filtering may be, and sometimes is, implemented on a patch basis. A candidate patch is generated by filtering a portion of an image captured by a camera, e.g., a second camera by performing a sharpening or blurring operation to a portion of the image to generate a candidate patch. The amount of blurring or sharpening that is applied to generate the candidate patch depends, in some embodiments, on the relative difference between the candidate patch and the reference patch.

Abstract: An image data frame is received from an external source. An error concealment operation is performed on the received image data frame in response to determining that a first frame size of the received image data frame is erroneous. The first frame size of the image data frame is determined to be erroneous based on at least one frame synchronization signal associated with the image data frame. An image processing operation is performed on the received image data frame on which the error concealment operation has been performed, thereby enabling an image processing module to perform the image processing operation without entering into a deadlock state and thereby prevent a host processor from having to execute hardware resets of deadlocked modules.

Abstract: An electronic device is provided. The electronic device includes a display, a processor functionally connected with the display, and a memory functionally connected with the processor. The memory stores instructions configured to, when executed, enable the processor to display a first image through the display, display one or more second images through the display while displaying the first image, select a third image from among the one or more second images, identify a value of at least one property of the third image, generate a filter for applying the value of the at least one property to an image, apply the value of the at least one property to the first image using the filter, display the first image, to which the value of the at least one property is applied, through the display, and store the filter in the memory.

Abstract: A processor-implemented method with blur estimation includes: acquiring size information of an input image; resizing the input image to generate a target image of a preset size; estimating a blur of the target image; and estimating a blur of the input image based on the size information of the input image.

Abstract: In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products for detecting motion in images. A computing system receives first and second images that were captured by a camera. The computing system generates, using the images, a mathematical transformation that indicates movement of the camera from the first image to the second image. The computing system generates, using the first image and the mathematical transformation, a modified version of the first image that presents the scene that was captured by the first image from a position of the camera when the second image was captured. The computing system determines a portion of the first image or second image at which a position of an object in the scene moved, by comparing the modified version of the first image to the second image.

Abstract: In a method of processing an image formed of pixels, skin tone pixels in the image are detected. Local sharpening is applied to at least some of the skin tone pixels. The image is regenerated using the locally sharpened skin tone pixels.

Abstract: In examples, a filter used to denoise shadows for a pixel(s) may be adapted based at least on variance in temporally accumulated ray-traced samples. A range of filter values for a spatiotemporal filter may be defined based on the variance and used to exclude temporal ray-traced samples that are outside of the range. Data used to compute a first moment of a distribution used to compute variance may be used to compute a second moment of the distribution. For binary signals, such as visibility, the first moment (e.g., accumulated mean) may be equivalent to a second moment (e.g., the mean squared). In further respects, spatial filtering of a pixel(s) may be skipped based on comparing the mean of variance of the pixel(s) to one or more thresholds and based on the accumulated number of values for the pixel.

Abstract: In one embodiment, a method includes obtaining an image comprising a plurality of pixels, determining, for a particular pixel of the plurality of pixels, a gradient value, classifying, based on the gradient value, the particular pixel into a flat class or one of a plurality of edge classes, and denoising the particular pixel based on the classification.

Abstract: Cross plane filtering may be used for enhanced chroma coding. An indication of a cross-plane filter associated with a current picture may be received. The current picture may include an intra-coded video block and a plurality of reference samples. The plurality of reference samples may be used to predict the intra-coded video block. A luma sample region may be determined in the current picture. The luma sample region may determined to enhance a corresponding chroma sample in the current picture. The cross-plane filter may be applied to a plurality of luma samples in the luma sample region to determine an offset. The cross-plane filter may be a high pass filter. The offset may be applied to the corresponding chroma sample to determine an enhanced chroma sample.

Abstract: Methods and systems include neural network-based image processing and blending circuitry to blend an output of the neural network to compensate for potential artifacts from the neural network-based image processing. The neural network(s) apply image processing to image data using one or more neural networks as processed data. Enhance circuitry enhances the image data in a scaling circuitry to generate enhanced data. Blending circuitry receives the processed image data and the enhanced data along with an image plane of the processed data. The blending circuitry also determines whether the image processing using the one or more neural networks has applied a change to the image data greater than a threshold amount. The blending circuitry then, based at least in part in response to the change being greater than the threshold amount and/or edge information of the image data, blends the processed data with the enhanced data.

Abstract: Methods and systems for reducing banding artifacts when displaying high-dynamic-range images are described. Given an input image in a first dynamic range, and an input backward reshaping function mapping codewords from the first dynamic range to a second dynamic range, wherein the second dynamic range is equal or higher than the first dynamic range, statistical data based on the input image and the input backward reshaping function are generated to estimate the risk of banding artifacts in a target image in the second dynamic range generated by applying the input backward reshaping function to the input image. Separate banding alleviation algorithms are applied in the darks and highlights parts of the first dynamic range to generate a modified backward reshaping function, which when applied to the input image to generate the target image eliminates or reduces banding in the target image.

Abstract: An asymmetric deblocking method for deblocking a boundary between a P block and a Q block such that 5 samples within the P block and 7 or 3 samples within the Q block are modified. The method includes determining a value refP based on at least p5, determining a value refQ based on at least qx, wherein qx is q3 or q7; determining a value refMiddle based on at least p0 and q0, wherein p0 is directly adjacent to the boundary and q0 is directly adjacent to the boundary; performing a linear interpolation between refP and refMiddle; and performing a linear interpolation between refQ and refMiddle.

Abstract: A digital data processor includes an instruction memory storing instructions each specifying a data processing operation and at least one data operand field, an instruction decoder coupled to the instruction memory for sequentially recalling instructions from the instruction memory and determining the data processing operation and the at least one data operand, and at least one operational unit coupled to a data register file and to an instruction decoder to perform a data processing operation upon at least one operand corresponding to an instruction decoded by the instruction decoder and storing results of the data processing operation. The operational unit is configured to increment histogram values in response to a histogram instruction by incrementing a bin entry at a specified location in a specified number of at least one histogram.

Abstract: A deblocking filtering method includes receiving reconstructed video data associated with a block boundary in a video coding system. The block boundary has N lines of samples crossing the block boundary from a P side to a Q side of the boundary. The method further includes determining whether to apply a first filter set to reduce block artifacts at the block boundary based on whether a first inter-side difference of a first line of the N lines of samples is greater than an inter-side difference threshold, determining a filter length of a filter in the first filter set based on a first side length of the P side, and a second side length of the Q side when it is determined to apply the first filter set, and applying at least one filter in the first filter set with the determined filter length on the block boundary.

Abstract: An image filtering method and apparatus are disclosed. The method includes: obtaining a first index of the luma blocks, wherein the first index represents a filter type applicable to the luma blocks; obtaining a second index of the first chroma blocks, wherein the second index represents a filter type applicable to the first chroma blocks; filtering the luma blocks by applying a first filter in a filter set, the first filter corresponding to the first index; and filtering the first chroma blocks by applying a second filter in the filter set, the second filer corresponding to the second index.

Abstract: Disclosed is a method of, and associated apparatus for, determining an edge position relating to an edge of a feature comprised within an image, such as a scanning electron microscope image, which comprises noise. The method comprises determining a reference signal from said image; and determining said edge position with respect to said reference signal. The reference signal may be determined from the image by applying a 1-dimensional low-pass filter to the image in a direction parallel to an initial contour estimating the edge position.

Abstract: The signal calculation unit calculates first, second, and third signal about an amount of blur or high frequency signal, which are related to an edge, based on a first luminance signal, a second luminance signal, and a third luminance signal, respectively. The data expansion unit expands the signals about an amount of blur or high frequency signal respectively to first, second, and third expanded signal data. A control method determination unit sets a back focus area based on a magnitude relation between the first expanded signal data and the second expanded signal data and set a front focus area based on magnitude relation between the first expanded signal data and the third expanded signal data to thereby control focus.

Abstract: A radiographic imaging apparatus includes an image generation unit that generates radiation images, and an image processing unit that synthesizes a plurality of radiation images to generate an enhanced image. The image processing unit is configured to synthesize the plurality of radiation images by weighting each of the plurality of radiation images in accordance with a feature point distance.

Abstract: A machine learning system for processing image data obtained from an image sensor is provided. The system includes a front end comprising one or more hard-coded filters, each of the one or more hard-coded filters being arranged to perform a set task. The system includes a neural network arranged to receive and process output from the front end. The one or more hard-coded filters include one or more hard-coded noise compensation filters that are hard-coded to compensate for a noise profile of the image sensor from which the image data is obtained. A method of processing image data in a machine learning system is also provided. A system for processing image data is provided.

Abstract: A video processing system and method apply a bilateral filter to images of a video stream in real time. The bilateral filter is executed and applied using a graphics processing unit (GPU) controlled by a processor. The bilateral filter may be encoded in a shader operated by the GPU. The GPU or processor may be configured to compress one or more video images of the video stream. Blurring or smoothing of the video images by the shader-implemented bilateral filter may reduce image noise thereby increasing a compression performance. The bilateral filter may be applied exclusively to a background of the video images which are substantially free of sharp edges. The video stream may be received from cameras covering an area of an airport, which may be an airport apron.

Abstract: A method is provided for operating a medical imaging X-ray device. The method records an earlier individual image of a series of images of a patient using a recording facility. The location of a medical component is identified, and the medical component is localized in the earlier individual image using a computing facility. A subregion of the earlier individual image is specified, determined by the position of the medical component localized in the earlier individual image and with the localized medical component represented. A later individual image is recorded and the medical component is localized in the later individual image. The later individual image is displayed, the later individual image averaged exclusively region-specifically in the region of the medical component with the subregion of the earlier individual image. Method acts are repeated with the displayed individual image as the earlier individual image to enhance visibility of the medical component.

Abstract: Disclosed herein are exemplary embodiments of methods, apparatus, and systems for performing content-adaptive deblocking to improve the visual quality of video images compressed using block-based motion-predictive video coding. For instance, in certain embodiments of the disclosed technology, edge information is obtained using global orientation energy edge detection (“OEED”) techniques on an initially deblocked image. OEED detection can provide a robust partition of local directional features (“LDFs”). For a local directional feature detected in the partition, a directional deblocking filter having an orientation corresponding to the orientation of the LDF can be used. The selected filter can have a filter orientation and activation thresholds that better preserve image details while reducing blocking artifacts. In certain embodiments, for a consecutive non-LDF region, extra smoothing can be imposed to suppress the visually severe blocking artifacts.

Abstract: Method for processing of a grey scale image, in particular a dim grey scale image, comprising the following steps: a) receiving an initial grey scale image, said initial grey scale image having a plurality of pixels at an initial resolution, b) calculating parameters characterizing the luminance (gain, median_grey, var_grey) and the noise level (X, noise_estimate, radius_spatial_summation, grid_size, threshold_var) of the initial grey scale image of step a), c) creating a basic intermediate image, d) creating an averaged intermediate image, and e) creating an enhanced grey scale image by interpolation of pixels based on the averaged receptors (greyAvg) of the averaged intermediate image of step d).

Abstract: A method of processing image data representative of an image using a multi-stage system comprising a first neural network (NN) for identifying a first image characteristic and a second NN for identifying a second image characteristic. The method comprises processing the image data using t a first at least one layer of the first NN to generate feature data representative of at least one feature of the image and processing the feature data using a second at least one layer of the first NN to generate first image characteristic data indicative of whether the image includes the first image characteristic. The feature data is transferred from the first NN to the second NN. The feature data is processed using the second NN to generate second image characteristic data representative of whether the image includes the second image characteristic.

Abstract: A cascading convolutional neural network (CCNN) comprising a plurality of convolutional neural networks (CNNs) that are trained by weighting training data based on loss values of each training datum between CNNs of the CCN. The CCNN can receiving an input image from plurality of images, classify the input image using the CCNN, and present a classification of the input image.

Abstract: A method for locating a golf ball including changing a temperature of a golf ball from a first temperature to a second temperature before use or marking the ball by reflective (mirror) or fluorescent material (e.g., NIR-IR fluorescent dye). The temperature changed ball is struck. Using either a thermal imaging camera with an imaging processing unit or a near-infrared (NIR) imaging camera with an imaging processing unit to produce a digital image of a part of the golf course with a potential golf ball location. An image processing technique is applied to produce an enhanced image of the golf ball location. A thermal imaging camera and a NIR imaging camera for locating a golf ball are described. A non-transitory computer readable media is described.

Abstract: Apparatus, systems and methods for adaptively reducing blocking artifacts in block-coded video are disclosed. In one implementation, a system includes processing logic at least capable of deblock filtering at least a portion of a line of video data based, at least in part, on edge information and texture information to generate at least a portion of a line of deblocked video data, and an image data output device responsive to the processing logic.

Abstract: Described is a system and method for dynamically changing encode quality at a block level based on runtime pre-encoding analysis of content in a video stream. A video encoder continuously analyzes the content during runtime, and collects statistics and/or characteristics of the content before encoding it. This classifies the block among pre-defined categories of content, where every category has its own compression parameters.

Abstract: Detecting breast abnormalities includes receiving a first mammographic image having original pixels. A second mammographic image is generated by enhancing the first mammographic image. Enhancing the first mammographic image includes performing the following for each original pixel in at least a subset of the original pixels. A histogram is generated for a region surrounding the original pixel, the region defined by an enhancement sliding window. Using the histogram, a value of the original pixel is revised to obtain a revised value, and the revised value is stored in the second mammographic image. A breast abnormality location is detected based on the second mammographic image.

Abstract: An image conversion device that performs conversion processing on image data before compression processing is performed includes: a pixel designation unit that sequentially designate, as a second pixel, pixels in a raster line that forms the image data in a direction away from a first pixel, which is a pixel included in the raster line, starting from a pixel adjacent to the first pixel in the raster line as a start point of the designation; an evaluation value calculation unit that calculates, as an evaluation value, an absolute value of a difference between pixel values of the first pixel and the second pixel or a square value of the difference; a determination unit that determines whether or not the evaluation value exceeds a predetermined threshold value; and a conversion unit that calculates a representative value of pixel values from the first pixel to a third pixel that is a pixel adjacent to the second pixel at a side of the first pixel and converts the pixel values from the first pixel to the third pi

Abstract: Disclosed is an image processing apparatus including: a sharpness recovery unit configured to recover sharpness of an input image; a correlation determination unit configured to determine a correlation between a target pixel and peripheral pixels of the input image; a noise removal unit configured to perform smoothing of the target pixel determined as having a correlation with the peripheral pixels in a recovered image output from the sharpness recovery unit; and a switching unit configured to switch between a mode in which the correlation determination unit uses the target pixel and the peripheral pixels of the pre-recovery input image in the correlation determination and a mode in which the correlation determination unit uses the target pixel and the peripheral pixels of the recovered input image in the correlation determination, depending on recovery characteristics used in the determination of the correlation determination unit.

Abstract: A system and method for enriching a concept database. The method includes determining, based on signatures of a first multimedia content element (MMCE) and signatures of a plurality of existing concepts in the concept database, at least one first concept, wherein each first concept is one of the plurality of existing concepts matching a portion of the first MMCE; generating a reduced representation of the first MMCE, wherein the generation of the reduced representation includes removing the at least one portion of the first MMCE matching the determined at least one first concept; comparing the reduced representation to signatures representing a plurality of second MMCEs to determine a plurality of matching second MMCEs; generating, based on the reduced representation and the plurality of matching second MMCEs, at least one second concept; and adding the generated at least one second concept to the concept database.

Abstract: A cascading convolutional neural network (CCNN) comprising a plurality of convolutional neural networks (CNNs) that are trained by weighting training data based on loss values of each training datum between CNNs of the CCN. The CCNN can receiving an input image from plurality of images, classify the input image using the CCNN, and present a classification of the input image.

Abstract: An image processing apparatus includes: a dispersion calculation unit for calculating a dispersion of pixel values in each of a plurality of pixel groups which are each composed of a pixel of interest and peripheral pixels around the pixel of interest in an image and in which the pixel of interest and the peripheral pixels are arranged in directions different from one another; and a gradation determination unit for determining whether or not the pixel of interest belongs to a gradation region on the basis of a magnitude relationship among the dispersions calculated by the dispersion calculation unit.

Abstract: A spatially adaptive quantization-aware deblocking filter is used for encoding or decoding video or image frames. The deblocking filter receives a reconstructed frame produced based on dequantized and inverse transformed coefficients of a video frame or an image frame. The reconstructed frame is filtered according to adaptive quantization field data for the video or image frame. The adaptive quantization field data represents weights applied to quantization values used at different areas of the video or image frame. A number of blocking artifacts remaining within the resulting filtered frame is determined. The adaptive quantization field data is then adjusted based on that number of blocking artifacts. The filtered frame is then filtered according to the adjusted adaptive quantization field data. The resulting re-filtered frame is then output to an output source, such as for transmission, display, storage, or further processing.

Abstract: Method and device for image rotation, and apparatus for image formation are provided. The method includes obtaining a tilt angle and size information of a to-be-processed image data, and obtaining position information of a target pixel corresponding to each rotated original pixel by performing a calculation based on the tilt angle and the size information and position information of each original pixel. The method also includes obtaining an enhancement parameter value and an edge parameter value by filtering the original pixel. Further, the method includes performing a two-dimensional interpolation process based on neighboring pixels of the original pixel, the enhancement parameter value and the edge parameter value, and obtaining a target pixel value of the target pixel according to an original pixel value of the original pixel, the enhancement parameter value, and the edge parameter value, thereby obtaining a target image corresponding to the rotated to-be-processed image data.

Abstract: An image processing apparatus includes an identification unit, a calculator, and an image processor. The identification unit identifies a group of pixels indicating an object of interest in medical image data. The calculator specifies a cross-section corresponding to the running direction of the object of interest indicated by the group of pixels in the medical image data. The image processor performs noise reduction on the cross-section identified.

Abstract: Apparatus, systems and methods for adaptively reducing blocking artifacts in block-coded video are disclosed. In one implementation, a system includes processing logic at least capable of deblock filtering at least a portion of a line of video data based, at least in part, on edge information and texture information to generate at least a portion of a line of deblocked video data, and an image data output device responsive to the processing logic.

Abstract: The present disclosure relates to an image processing apparatus and method and decoding apparatus. The image processing apparatus may comprises a receiver for receiving an image and an image processor for dividing the image into a plurality of regions, and performing filtering by iteratively applying at least one filter to each of the plurality of regions in the image, wherein the at least one filter comprises an asymmetric filter that uses an asymmetric filtering window having height and width in different size.

Abstract: A digital filter has an assigned filter function with assigned filter coefficients, an input receiving input samples, another input receiving confidence values, and an output. Each input sample value is associated to an input confidence value, wherein the filter output depends on the input samples, the input confidence values as well as the filter coefficients. The filter contains multiple accumulators, wherein an output sample is produced after a predetermined number of sample values wherein associated confidence values have been input to the filter.

Abstract: Techniques herein are for navigation data structures for graph traversal. In an embodiment, navigation data structures that a computer stores include: a source vertex array of vertices; a neighbor array of dense identifiers of target vertices terminating edges; a bidirectional map associating, for each vertex, a sparse identifier of the vertex with a dense identifier of the vertex; and a vertex array containing, when a dense identifier of a source vertex is used as an offset, a pair of offsets defining an offset range, for use with the neighbor array. The source vertex array, using the dense identifier of a particular vertex as an offset, contains an offset, into a neighbor array, of a target vertex terminating an edge originating at the particular vertex. The neighbor array contiguously stores dense identifiers of target vertices terminating edges originating from a same source vertex.

Abstract: An image processing method comprises: identifying a weak edge comprising a plurality of weak edge pixels and a strong edge comprising a plurality of strong edge pixels in an input image; filtering at least a part of said input image to obtain a smoothed image, during which said weak edge in said input image is filtered with a first filter and said strong edge in said input image is filtered with a second filter having a smoothness less than that of said first filter; acquiring edge information of said input image based on said input image and said smoothed image; and generating an output image based on said edge information and said smoothed image. The present invention further relates to an image processing system and an imaging system.

Abstract: An image processing apparatus and method including executing high-pass filtering in a column direction on the pixel values of original image data read from a flat panel detector to obtain first image data, and subtracting a value obtained by converting each of the pixel values of the first image data in accordance with an absolute value of a statistic calculated from pixel values in the same pixel row of the first image data from the value of a corresponding pixel of the original image data to obtain processed image data.

Abstract: A data compression method wherein said data comprises noise and information, comprising a data acquisition step, a pre-compression parameter selection step, wherein said pre-compression parameters are linked to an information loss, a compression step, and a storage step, the compression step being characterized in that it comprises a lossy pre-compression for removing some noise of the data, carried out using the pre-compression parameters selected in the selection step followed by lossless compression for compressing the remaining data.

Abstract: Depth based block partitioning in high efficiency video coding is provided by partitioning a video image block into different partitions using a binary segmentation mask. A determination is made whether to filter pixels at a boundary between the partitions. A particular pixel is not filtered in response to each adjacent pixel in vertical and horizontal planes in relation to the particular pixel having a same value. The particular pixel is filtered in response to any adjacent pixel in the vertical and horizontal planes in relation to the particular pixel having a different value than any other adjacent pixel in the vertical and horizontal planes in relation to the particular pixel. Pixels are filtered pursuant to a filtering process in response to a filtering determination.

Abstract: A content receiver and a method for controlling the same are provided. The content receiver, for example, may include, but is not limited to, a communication system configured to receive video content comprising a plurality of video frames from a content source, and a video analyzer configured to determine when compression of the video content is causing blockiness in one or more of the plurality of video frames, and trigger at least one content receiver response when the compression of the video content is causing blockiness in one or more of the plurality of video frames.

Abstract: A system and process of generating an improved image for optical character recognition is disclosed comprising dynamically obtaining a video source image comprising one or more characters on a background using a video capture device; detecting edges of at least one of the one or more characters that comprise the video source image; determining a sharpness value for the edges of the at least one of the one or more characters, wherein the sharpness value is related to the ability of the video capture device to distinguish the edges of the characters from the background; and indicating at least when the sharpness value is at a predetermined value or within a predetermined range of values.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for upscaling an image. One of the methods includes upscaling a low resolution image, creating first pixel subsets of the first upscaled image, creating second pixel subsets of a high resolution image, determining, for each subset in the pixel subsets, a value of a property of the pixel subset, determining, for each subset in the pixel subsets, a group of subsets to which the corresponding pixel subset belongs using the value of the property, and determining, for each of the groups of subsets, a filter to apply to each of the first pixel subsets that correspond to the pixel subsets in the group to create a final pixel subset that approximates the corresponding second pixel subset using the first pixel subset, a combination of all of the final pixel subsets representing a second upscaled image.