Abstract: An image signal processor may include a pixel defect correction component that tracks defect history for frames captured by an image sensor and applies the history when identifying and correcting defective pixels in a frame. The component maintains a defect pixel location table that includes a defect confidence value for pixels of the image sensor. The component identifies defective pixels in a frame, for example by comparing each pixel's value to the values of its neighbor pixels. If a pixel is detected as defective, its defect confidence value may be incremented. Otherwise, the value may be decremented. If a pixel's defect confidence value is over a defect confidence threshold, the pixel is considered defective and thus may be corrected. If a pixel's defect confidence value is under the threshold, the pixel is considered not defective and thus may not be corrected even if the pixel was detected as defective.

Abstract: An image processing pipeline may dynamically determine filtering strengths for noise filtering of image data. Statistics may be collected for an image at an image processing pipeline. The statistics may be accessed and evaluated to generate a filter strength model that maps respective filtering strengths to different portions of the image. A noise filter may determine a filtering strength for image data received at the noise filter according to the filter strength model. The noise filter may then apply a filtering technique according to the determined filtering strength.

Abstract: Systems and methods for automatic lens flare compensation may include a non-uniformity detector configured to operate on pixel data for an image in an image sensor color pattern. The non-uniformity detector may detect a non-uniformity in the pixel data in a color channel of the image sensor color pattern. The non-uniformity detector may generate output including location and magnitude values of the non-uniformity. A lens flare detector may determine, based at least on the location and magnitude values, whether the output of the non-uniformity detector corresponds to a lens flare in the image. In some embodiments, the lens flare detector may generate, in response to determining that the output corresponds to the lens flare, a representative map of the lens flare. A lens flare corrector may determine one or more pixel data correction values corresponding to the lens flare and apply the pixel data correction values to the pixel data.

Abstract: An image processing pipeline may dynamically determine filtering strengths for noise filtering of image data. Statistics may be collected for an image at an image processing pipeline. The statistics may be accessed and evaluated to generate a filter strength model that maps respective filtering strengths to different portions of the image. A noise filter may determine a filtering strength for image data received at the noise filter according to the filter strength model. The noise filter may then apply a filtering technique according to the determined filtering strength.

Abstract: The present disclosure provides an image capture, curation, and editing system that includes a resource-efficient mobile image capture device that continuously captures images. In particular, the present disclosure provides low power frameworks for controlling image sensor mode in a mobile image capture device. On example low power frame work includes a scene analyzer that analyzes a scene depicted by a first image and, based at least in part on such analysis, causes an image sensor control signal to be provided to an image sensor to adjust at least one of the frame rate and the resolution of the image sensor.

Abstract: An apparatus is described. The apparatus includes a smart image sensor having a memory and a processor that are locally integrated with an image sensor. The memory is to store first program code to be executed by the processor. The memory is coupled to the image sensor and the processor. The memory is to store second program code to be executed by the processor. The first program code is to cause the smart image sensor to perform an analysis on one or more images captured by the image sensor. The analysis identifies a region of interest within the one or more images with machine learning from previously captured images. The second program code is to cause the smart image sensor to change an image sensing and/or optical parameter in response to the analysis of the one or more images performed by the execution of the first program code.

Abstract: A pedestal level for an image sensor can be dynamically adjusted based on one or more parameters. The parameters include one or more operating conditions associated with the image sensor, pre-determined image sensor characterization data, the number of unused digital codes, and/or the number of clipped pixel signals. The operating conditions can include the temperature of the image sensor, the gain of at least one amplifier included in processing circuitry operably connected to at least one pixel, and/or the length of the integration period for at least one pixel in the image sensor. Based on the one or more of the parameters, the pedestal level is adjusted to reduce a number of unused digital codes in a distribution of dark current. Additionally or alternatively, the variance of the pixel signals can be reduced to permit the use of a lower pedestal level.

Abstract: The present disclosure provides image capture devices and associated methods that feature intelligent use of hardware-generated statistics. An example image capture device can include an imaging hardware pipeline that generates frames of imagery. The imaging hardware pipeline can generate one or more hardware-generated statistics based at least in part on, for example, the raw image data captured by the image sensor or intermediate image data within the pipeline. The image capture device can analyze the hardware-generated statistics to determine one or more metrics for the raw image data or the image. The image capture device can determine a downstream operation of the image capture device relative to the image based at least in part on the metrics determined from the hardware generated statistics.

Abstract: An apparatus is described. The apparatus includes a smart image sensor having a memory and a processor that are locally integrated with an image sensor. The memory is to store first program code to be executed by the processor. The memory is coupled to the image sensor and the processor. The memory is to store second program code to be executed by the processor. The first program code is to cause the smart image sensor to perform an analysis on one or more images captured by the image sensor. The analysis identifies a region of interest within the one or more images with machine learning from previously captured images. The second program code is to cause the smart image sensor to change an image sensing and/or optical parameter in response to the analysis of the one or more images performed by the execution of the first program code. Alternatively or in combination, the memory is to store third program code to be executed by the processor and fourth program code to be executed by the processor.

Abstract: In an embodiment, an electronic device may be configured to capture still frames during video capture but may capture the still frames in the 4×3 aspect ratio and at higher resolution than the 16×9 aspect ratio video frames. The device may interleave high resolution, 4×3 frames and lower resolution 16×9 frames in the video sequence, and may capture the nearest higher resolution, 4×3 frame when the user indicates the capture of a still frame. Alternatively, the device may display 16×9 frames in the video sequence, and then expand to 4×3 frames when a shutter button is pressed. The device may capture the still frame and return to the 16×9 video frames responsive to a release of the shutter button.

Abstract: Embodiments of the present disclosure relate to highlight recovery of a high-resolution image using a single low-resolution image captured at a lower exposure. An example apparatus includes a hue target circuit that receives an input image at a high-resolution including at least one pixel with a clipped color channel. For example, the input image is a Blue sky image with a pixel having clipped Blue channel. The hue target circuit also receives a set of candidate hue maps having a pixel resolution lower than the high-resolution of the input image. The hue target circuit generates a target hue value for the at least one pixel using the pixel information of the set of candidate hue maps. The apparatus also includes a hue recovery circuit that generates a recovered version of the input image by adjusting hue information of the clipped color channel based on the generated target hue.

Abstract: Embodiments relate to a histogram-of-oriented gradients (HOG) module. The HOG module is implemented in hardware rather than software. The HOG module applies an algorithm to an image to identify gradient orientation in localized portions of the image. The HOG module creates a histogram-of orientation gradients based on the identified gradient orientations.

Abstract: In an embodiment, an electronic device may be configured to capture still frames during video capture, but may capture the still frames in the 4×3 aspect ratio and at higher resolution than the 16×9 aspect ratio video frames. The device may interleave high resolution, 4×3 frames and lower resolution 16×9 frames in the video sequence, and may capture the nearest higher resolution, 4×3 frame when the user indicates the capture of a still frame. Alternatively, the device may display 16×9 frames in the video sequence, and then expand to 4×3 frames when a shutter button is pressed. The device may capture the still frame and return to the 16×9 video frames responsive to a release of the shutter button.

Abstract: Embodiments relate to an architecture of a vision pipe included in an image signal processor. The architecture includes a front-end portion that includes a pair of image signal pipelines that generate an updated luminance image data. A back-end portion of the vision pipe architecture receives the updated luminance images from the front-end portion and performs, in parallel, scaling and various computer vision operations on the updated luminance image data. The back-end portion may repeatedly perform this parallel operation of computer vision operations on successively scaled luminance images to generate a pyramid image.

Abstract: Embodiments relate to a histogram-of-oriented gradients (HOG) module. The HOG module is implemented in hardware rather than software. The HOG module applies an algorithm to an image to identify gradient orientation in localized portions of the image. The HOG module creates a histogram-of orientation gradients based on the identified gradient orientations.

Abstract: Embodiments relate to color correction circuit operations performed by an image signal processor. The color correction circuit computes optimal color correction matrix on a per-pixel basis and adjusts it based on relative noise standard deviations of the color channels to steer the matrix.

Abstract: Embodiments of the present disclosure relate to a configurable convolution engine that receives configuration information to perform convolution or its variant operations on streaming input data of various formats. To process streaming input data, input data of multiple channels are received and stored in an input buffer circuit in an interleaved manner. Data values of the interleaved input data are retrieved and forwarded to multiplier circuits where multiplication with a corresponding filter element of a kernel is performed. Varying number of kernels with different sizes and sparsity can also be used for the convolution operations.

Abstract: An image processing pipeline may apply chroma suppression to image data at a scaler implemented in the image processing pipeline. Image data collected for an image may be received at a scaler that is encoded in a color space that includes a luminance component and chrominance components. When resampling the image data to generate a different size of the image, the scaler may attenuate the chrominance components of the image data according to the luminance component of the image data. The scaler may also perform dot error correction and convert the image data from one subsampling scheme to another.

Abstract: An image processing pipeline may process image data at multiple rates. A stream of raw pixel data collected from an image sensor for an image frame may be processed through one or more pipeline stages of an image signal processor. The stream of raw pixel data may then be converted into a full-color domain and scaled to a data size that is less than an initial data size for the image frame. The converted pixel data may be processed through one or more other pipelines stages and output for storage, further processing, or display. In some embodiments, a back-end interface may be implemented as part of the image signal processor via which image data collected from sources other than the image sensor may be received and processed through various pipeline stages at the image signal processor.

Abstract: Embodiments relate to color correction circuit operations performed by an image signal processor. The color correction circuit computes optimal color correction matrix on a per-pixel basis and adjusts it based on relative noise standard deviations of the color channels to steer the matrix.