Texas Instruments Corporation

Gang Hua

Mihir Narendra Mody

Niraj Nandan

Rajasekhar Reddy Allu

Shashank Dabral

Mayank Mangla

Hrushikesh Tukaram Garud

Pandy Kalimuthu

Hetul Sanghvi

Denis R Beaudoin

Brian Okchon Chae

Jing-Fei Ren

Sujith Shivalingappa

Vikram VijayanBabu Appia

Brijesh Rameshbhai Jadav

Denis Roland Beaudoin

Ankur Ankur

Gunawath Dilshan Godaliyadda

In some examples, a method comprises receiving pixel data from an image capture device having a color filter, wherein the pixel data represents a portion of an image. The method further includes performing wavelet decomposition on the pixel data to produce decomposed pixel data and determining a local intensity of the pixel data. The method also includes determining a noise threshold value based on the local intensity and a noise intensity function that is based on the color filter; determining a noise value for the pixel data based on the decomposed pixel data and the noise threshold value; and correcting the pixel data based on the noise value to produce an output image.

Noise estimation using user-configurable information

A system is provided. The system generally includes a first processor configured to receive image input data from a red-green-blue infrared (RGBIR) sensor. The first processor of the system is configured to generate a first intermediate image data from the image input data. The system generally includes a second processor. The second processor of the system is configured to generate a second intermediate image data that includes red-green-blue (RGB) image data from the first intermediate image data, and to generate a third intermediate image data that includes infrared (IR) image data from the first intermediate image data. The system generally includes a third processor. The third processor of the system is configured to process the third intermediate image data. The system generally includes a fourth processor. The fourth processor of the system is configured to process the second image data.

RGBIR color filter image processing with lens shading correction

Various embodiments disclosed herein relate to defective pixel detection and correction, and more specifically to using threshold functions based on color channels to compare pixel values to threshold values. A method is provided herein that comprises identifying a color channel of an image pixel in a frame and identifying a threshold function based at least on the color channel. The method further comprises applying the threshold function to one or more nearest-neighbor values to obtain a threshold value and determining whether a corresponding sensor pixel is defective based at least on a comparison of the image pixel to the threshold value.

Defective pixel detection

Disclosed herein are improvements to pixel pattern conversion, upsampling, and IR decontamination processes. An example includes an image processing pipeline comprising an upstream component, a pattern conversion component downstream with respect to the upstream component in the image processing pipeline, and a downstream component relative to the pattern conversion component. The pattern conversion component is configured to obtain RGB-IR pixel data produced by the upstream component of the image processing pipeline and convert the RGB-IR pixel data into RGB pixel data and IR pixel data using a conversion engine. The conversion engine is configured to demosaic the RGB-IR pixel into the RGB pixel data and the IR pixel data, remosaic the RGB pixel data into an RGB pattern and the IR pixel data into an IR pattern and remove IR contamination from the RGB pixel data of the RGB pattern for use by the downstream component.

RGB-IR pixel pattern conversion via conversion engine

A method for error handling in a geometric correction engine (GCE) is provided that includes receiving configuration parameters by the GCE, generating, by the GCE in accordance with the configuration parameters, output blocks of an output frame based on corresponding blocks of an input frame, detecting, by the GCE, a run-time error during the generating, and reporting, by the GCE, an event corresponding to the run-time error.

Error handling in a geometric correction engine

In an advanced driver-assistance system (ADAS), RAW sensor image processing for a machine vision (MV) application is important. Due to different color, e.g., red/green/blue (RGB), color components, being focused by the lens at different locations in image plane, the lateral chromatic aberration phenomenon may sometimes be observed, which causes false color around edges in the final image output, especially for high contrast edges, which can impede MV applications. Disclosed herein are low-latency, efficient, optimized designs for chromatic aberration correction (CAC) components. An in-pipeline CAC design may be used to perform on-the-fly CAC without any out-of-pipeline memory traffic; enable use of wide dynamic range (WDR) sensors; uses bicubic interpolation; support vertical and horizontal chromatic aberration color channel offsets, reduce CAC line memory requirements, and support flexible look-up table (LUT) down-sampling factors to improve the spatial precision of correction and accommodate popular image sensor resolutions.

In-line chromatic aberration correction in wide dynamic range (WDR) image processing pipeline

A technique for image processing, comprising: receiving input image data, wherein the image data is companded into a first bit depth, wherein the image data includes incomplete color values for pixels of the image data, and wherein the image data is associated with a first color space, interpolating the image data to generate color values for the incomplete color values for pixels of the image data, expanding the image data from the first bit depth to a second bit depth, wherein the color values of the expanded image data have a linear dynamic range, and wherein the second bit depth is higher than the first bit depth, converting the color values for pixels of the expanded image data from the first color space to a second color space, and compressing the color values for pixels of the image data to a third bit depth, the third bit depth lower than the second bit depth, and wherein the compressed color values have a nonlinear dynamic range.

High precision color processing for wide dynamic range sensors

Local automatic white balance (AWB) of wide dynamic range (WDR) images is provided. Methods and systems include collecting, by an image signal processor (ISP), statistics for local AWB from at least one wide dynamic range (WDR) image received by the ISP; generating, by a processor, based on the statistics, local gain lookup tables (LUTs), one for each color channel represented in the WDR image(s), each local gain LUT providing a correlation between gain and intensity; and storing the local gain LUTs. Further processing includes, for each of multiple pixels of a WDR image to be output calculating an intensity value, accessing the local gain LUT for the color channel corresponding to that pixel using the calculated intensity value to identify a corresponding local gain value, and applying the local gain value to that pixel.

Intensity separated local white balance correction

Techniques for image processing including receiving input image data, wherein the input image data includes data associated with a clear color channel, receiving a color offset value associated with a color channel, wherein color values for the color channel are not provided in the input image data, based on the color offset value, generating intermediate estimated color values for the color channel, wherein generating the intermediate estimated color values includes: clipping color values that have a magnitude greater than the color offset value, and adjusting color values that have a magnitude less than the color offset value based on the color offset value, applying a color correction function to the intermediate estimated color values based on the color offset value to determine color corrected estimated color values, and outputting the color corrected estimated color values.

Suppression of clipping artifacts from color conversion

A method for geometrically correcting a distorted input frame and generating an undistorted output frame includes capturing and storing an input frame in an external memory, allocating an output frame with an output frame size and dividing the output frame into output blocks, computing a size of the input blocks in the input image corresponding to each output blocks, checking if the size of the input blocks is less than the size of the internal memory and if not dividing until the required input block size of divided sub blocks is less than the size of the internal memory, programming an apparatus with input parameters, fetching the input blocks into an internal memory, processing each of the divided sub blocks sequentially and processing the next output block in step until all the output blocks are processed; and composing the output frame for each of the blocks in the output frame.

Method and apparatus for dynamic block partition of an image

A technique for determining regions and block sizes for configuring a perspective transformation engine including determining a set of scale ratios for images captured by a camera, generating a scale ratio image based on the set of scale ratios, determining a set of boundary ranges for the scale ratio image, generating a binary scale ratio image using the set of scale ratios of the scale ratio image, determining a set of regions based on the set of boundary ranges for the binary scale ratio image, determining a block size for each region of the determined set of regions, and outputting the determined set of regions and the determined block sizes.

Dynamic configuration of perspective transformation engine

A method of de-mosaicing pixel data from an image processor includes generating a pixel block that includes a plurality of image pixels. The method also includes determining a first image gradient between a first set of pixels of the pixel block and a second image gradient between a second set of pixels of the pixel block. The method also includes determining a first adaptive threshold value based on intensity of a third set of pixels of the pixel block. The pixels of the third set of pixels are adjacent to one another. The method also includes filtering the pixel block in a vertical, horizontal, or neutral direction based on the first and second image gradients and the first adaptive threshold value utilizing a plurality of FIR filters to generate a plurality of component images.

Universal and adaptive de-mosaicing (CFA) system

A method for automatic exposure (AE) control is provided that includes receiving statistics for AE control for an image from an image signal processor (ISP) coupled to an image sensor generating the image, computing an exposure value at a current time t (EV(t)) using a cost function based on target characteristics of an image, wherein computation of the cost function uses the statistics, and computing AE settings for the image sensor based on EV(t).

Method and apparatus for automatic exposure

An apparatus and method for geometrically correcting an arbitrary shaped input frame and generating an undistorted output frame. The method includes capturing arbitrary shaped input images with multiple optical devices and processing the images, identifying redundant blocks and valid blocks in each of the images, allocating an output frame with an output frame size and dividing the output frame into regions shaped as a rectangle, programming the apparatus and disabling processing for invalid blocks in each of the regions, fetching data corresponding to each of the valid blocks and storing in an internal memory, interpolating data for each of the regions with stitching and composing the valid blocks for the output frame and displaying the output frame on a display module.

Growing community of inventors

Katy, TX, United States of America

Gang Hua