Arm Limited

Apical Limited

David Hanwell

Alexey Kornienko

Maxim Novikov

Viacheslav Chesnokov

Alex Kornienko

Nerhun Yildiz

Andrew Dewhurst

Dumidu Sanjaya Talagala

Puneet Singh Matharu

Alexis Leonardo Lluis Gomez

Yury Khrustalev

Alassane Seck

James Stuart Imber

Laurence Mark Arden

A method of processing image data, the image data comprising a plurality of data values arranged in two or more sequences, each sequence comprising a first number of data positions each having a data value of the plurality, and each data value comprising a second number of bits, the method comprising: upon receiving each sequence of the two or more sequences, processing the data values of the sequence to determine, for each given data position of the first number of data positions, a representative value for the given data position based on a relationship between the data value at the given data position and the data value at one or more neighbouring data positions.

Methods and apparatus for image processing

Example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, techniques to process pixel values sampled from a multi color channel imaging device. In particular, methods and/or techniques to process pixel samples for interpolating pixel values for one or more color channels.

System, devices and/or processes for processing image pixel values

An image processing method including obtaining image data. The image data includes a plurality of image data values. The image processing method also includes processing the image data, thereby generating output data. Processing the image data includes applying a convolution operation to the plurality of image data values using a kernel including a plurality of coefficients. Applying the convolution operation includes obtaining a sum of image data values of the plurality of image data values that correspond respectively to coefficients of the plurality of coefficients that each have a common coefficient value. Applying the convolution operation also includes multiplying the sum by the common coefficient value.

Image processing method and system

Example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, techniques to process pixel values sampled from a multi color channel imaging device. In particular, methods and/or techniques to process pixel samples for non-visible light from pixels allocated to detection of infrared light are disclosed.

System, devices and/or processes for processing infrared pixel values

A method comprising obtaining image data representative of pixel intensities associated with an image. Irrespective of a respective magnitude of the pixel intensities, the image data is processed with a logarithmic transformation to transform the pixel intensities to transformed pixel intensities, thereby generating transformed image data representative of the transformed pixel intensities. The logarithmic transformation, L(x), is representable as:()=log(())+where x represents a pixel intensity to be transformed, f(x) represents a function of x and P, M and N represent constants.

Image processing

An image processor comprising an input module for receiving image data from an image sensor; an image processing module arranged to perform one or more operations on at least a portion of the image data to generate processed image data; and a characteristic processing module arranged to perform one or more characteristic processing operations on at least a portion of the characteristic data to generate processed characteristic data. The portion of the characteristic data is associated with the portion of the image data; the one or more characteristic processing operations are associated with the one or more image processing operations. The image processor further comprises an output module for outputting the processed image data and processed characteristic data.

Image data pre-processing for neural networks

A method for detecting and correcting transient faults, the method the steps of comprising obtaining () image data from a camera system, and processing () image data using a first image signal processor and a second image signal processor, to produce first and second output data. At least one statistical model is generated () based on at least the first and second output data and used to identify () whether a fault is present in the first output data. A correction value for the portion of image data is generated (), wherein the correction value is an expected value based on the statistical models, and used to generate () updated output data. The updated output data is then outputted () to an output device.

System and method for fault detection and correction

A method of optimising an image signal processor (ISP), which is to be used to process sensor image data generating output image data. The method may include obtaining sensor image data; processing the sensor image data according to one or more ISP settings to produce output image data; producing quality metric data associated with the output image data and optimising the one or more ISP settings based on the quality metric data.

Image signal processing

The present disclosure provides an image processing apparatus and system which downscales an image which is generated from data provided by a sensor. The downscaled image is then analyzed to determine the location of one or more regions of interest in the image. The regions of interest can then be cropped from the original image and those cropped regions of interest processed by a computer vision engine.

A method and apparatus for image processing, the method comprising obtaining input image data comprising a plurality of pixel intensity values representing a respective plurality of pixel locations, obtaining pixel location data identifying one or more pixel locations represented by compromised pixel intensity values, generating interpolated image data comprising improved pixel intensity values, storing improved image data comprising at least the interpolated image data, and detecting one or more further compromised pixel intensity values based on the improved image data.

Pixel correction

A method, processor and system of retaining dynamic range. The method comprises obtaining a vignetting correction mesh for image data. The method also comprises generating an intensity compression curve, the intensity compression curve comprising one or more segments, based on the vignetting correction mesh; and adjusting pixel values of a portion of the image data according to the intensity compression curve and vignetting correction mesh.

A method and apparatus for processing image data is provided. The method comprises storing accumulated image data, obtaining a new frame of image data, performing motion detection based at least on the new frame of image data, updating the accumulated frame of image data, and generating output image data. The motion detection identifies a pixel location in which motion is detected. For the pixel location identified by the motion detection, a pixel intensity value of the new frame of image data contributes more to a pixel intensity value representing the identified pixel location in the updated accumulated imaged data than to a pixel intensity value representing the identified pixel location in the output image data.

A method of calibrating image data, the method comprising the steps of obtaining the image data and applying a shading correction mesh to the image data, wherein the shading correction mesh comprises a plurality of nodes, and is used to generate shading correction values for each pixel location in the image data. The blocks of the generated shading correction values are then grouped each group of blocks comprising a plurality of blocks, and each block comprising a plurality of pixel locations. An analysis of each of the groups of blocks of generated shading correction values is performed, and an updated shading correction mesh based on the analysis of the groups of one or more blocks is generated.

Methods and apparatus for generating improved image data from received input image data comprising first input image data associated with a first exposure level and second input image data associated with a second, different, exposure level. Motion detection data is generated from the received input image data by applying a noise model and improved image data is generated by combining data from the first and second input data in dependence on the motion detection data.

Spatially multiplexed exposure

Image data representative of at least a portion of an image comprising first and second sets of pixels associated with first and second colour channels, respectively, is received. It is determined that a first pixel of the first set of pixels is a saturated pixel. First data based on a value of the first pixel of the second set of pixels is obtained for a first image region comprising the first pixel of the first set of pixels and a first pixel of the second set of pixels. Second data based on respective values of second pixels of the first and second sets of pixels are obtained, based on the first data, for a second image region. The second data is processed to generate output data representative of an estimated value associated with the first colour channel for the first pixel.

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Stapleford, United Kingdom

David Hanwell