Abstract: Systems, methods, and computer-readable media are provided for low power, variable focus. An example method can include obtaining, based on a trigger, a first image of a scene captured by an image sensor, the first image being captured with a lens of the image sensor in a first configuration of a plurality of available lens configurations; determining, based on the first image and a first detection result, whether an object of interest is present in the first image; in response to determining that the object of interest is not present in the first image, adjusting the lens to a second configuration selected from the plurality of lens configurations; obtaining, by the image sensor, a second image of the scene while the lens is in the second configuration; and determining, based on the second image and a second detection result, that the object of interest is present in the second image.

Abstract: Methods, systems, and apparatuses for image segmentation are provided. For example, a computing device may obtain an image, and may apply a process to the image to generate input image feature data and input image segmentation data. Further, the computing device may obtain reference image feature data and reference image classification data for a plurality of reference images. The computing device may generate reference image segmentation data based on the reference image feature data, the reference image classification data, and the input image feature data. The computing device may further blend the input image segmentation data and the reference image segmentation data to generate blended image segmentation data. The computing device may store the blended image segmentation data within a data repository. In some examples, the computing device provides the blended image segmentation data for display.

Abstract: Systems and techniques are described herein for displaying information. For instance, a device for displaying information is provided. The device may include at least one memory; and at least one processor coupled to the at least one memory and configured to: detect an object in an image of a scene obtained from a first camera; determine that a user is gazing at a representation of the object displayed at a display based on an image of the user obtained from a second camera; and based on determining that the user is gazing at the representation of the object displayed at the display, display, via the display, information associated with the object.

Abstract: A processor-implemented method for multiplication approximation includes receiving inputs to be processed using an artificial intelligence (AI) compute engine. The inputs have a first precision. The AI compute engine is configured for processing in a second precision different from the first precision. A first parameter for the inputs and a second parameter for the AI compute engine are defined. The first parameter and the second parameter respectively indicate a first portion of the first precision and a second portion of the second precision to use for computation by the AI compute engine. The inputs and the second set of compute engine parameters are respectively adapted according to the first parameter and the second parameter to generate a first representation and a second representation. An approximation of an AI workload for the inputs is generated based on the first representation and the second representation.

Abstract: Systems and techniques are provided for processing one or more frames. For example, a process can include obtaining a first plurality of frames associated with a first settings domain from an image capture system, wherein the first plurality of frames is captured prior to obtaining a capture input. The process can include obtaining a reference frame associated with a second settings domain from the image capture system, wherein the reference frame is captured proximate to obtaining the capture input. The process can include obtaining a second plurality of frames associated with the second settings domain from the image capture system, wherein the second plurality of frames is captured after the reference frame. The process can include, based on the reference frame, transforming at least a portion of the first plurality of frames to generate a transformed plurality of frames associated with the second settings domain.

Abstract: Systems and techniques are described herein for generating image content. For instance, an apparatus for generating image content is provided. The method may include a user interface configured to: display an image of a field of view; and receive a user input indicative of a desired change to the image, wherein the desired change to the image comprises a change to the field of view; and at least one processor configured to: provide at least part of the image and an indication of the desired change as inputs to a generative machine-learning model; obtain an altered image from the generative machine-learning model, wherein the altered image comprises at least part of the image of the field of view and generated pixels outside of the field of view.

Abstract: This disclosure provides systems, methods, and devices for image signal processing that support image signal processing of exposure bracketed image frames. In a first aspect, a method of image processing includes receiving, by at least one processor, image data comprising first image data of a first exposure duration, second image data of a second exposure duration, and third image data of a third exposure duration, wherein the first exposure duration is equal to the third exposure duration; determining, by the at least one processor, fourth image data by subtracting corresponding pixel intensity values of the third image data from the second image data; and determining, by the at least one processor, a first output image frame by combining the fourth image data with the first image data. Other aspects and features are also claimed and described.

Abstract: This disclosure provides systems, methods, and devices for image signal processing that support high dynamic range (HDR) image processing on image frames with temporally-aligned centers to reduce artifacts resulting from fusing image frames with different temporal centers. In some aspects, a method of image processing includes capturing three or more image frames having at least two different exposure lengths. The three or more image frames are processed to obtain two image frames with temporally-aligned centers, and those two image frames are processed in HDR fusion logic to obtain an output HDR image frame. Other aspects and features are also claimed and described.

Abstract: Systems and techniques are described herein for Processing data. For instance, a method for processing data is provided. The method may include: obtaining image data having a first resolution; downsampling the image data to generate downsampled image data, wherein the downsampled image data has a second resolution that is lower than the first resolution; processing the downsampled image data to generate processed downsampled image data; and generating, using a machine-learning model, upsampled image data based on the processed downsampled image data and the image data.

Abstract: Systems and techniques are provided for processing one or more frames. For example, a process can include obtaining a first plurality of frames associated with a first settings domain from an image capture system, wherein the first plurality of frames is captured prior to obtaining a capture input. The process can include obtaining a reference frame associated with a second settings domain from the image capture system, wherein the reference frame is captured proximate to obtaining the capture input. The process can include obtaining a second plurality of frames associated with the second settings domain from the image capture system, wherein the second plurality of frames is captured after the reference frame. The process can include, based on the reference frame, transforming at least a portion of the first plurality of frames to generate a transformed plurality of frames associated with the second settings domain.

Abstract: An imaging system can obtain image data, for instance from an image sensor. The imaging system can supply the image data as input data to a machine learning system, which can generate one or more maps based on the image data. Each map can identify strengths at which a certain image processing function is to be applied to each pixel of the image data. Different maps can be generated for different image processing functions, such as noise reduction, sharpening, or color saturation. The imaging system can generate a modified image based on the image data and the one or more maps, for instance by applying each of one or more image processing functions in accordance with each of the one or more maps. The imaging system can supply the image data and the one or more maps to a second machine learning system to generate the modified image.

Abstract: Systems and techniques are described herein for processing data (e.g., image data) using convolution as a transformer (CAT) operations. The method includes receiving, at a convolution engine of a machine learning system, a first set of features, the first set of features being associated with an image and having a three-dimensional shape, applying, via the convolution engine, a depth-wise separable convolutional filter to the first set of features to generate a first output, applying, via the convolution engine, a pointwise convolutional filter to the first output to generate a second output based on global information from a spatial dimension and a channel dimension associated with the image, modifying the second output to the three-dimensional shape to generate a second set of features and combining the first set of features and the second set of features to generate an output set of features.

Abstract: Systems, methods, and non-transitory media are provided for predictive camera initialization. An example method can include obtain, from a first image capture device, image data depicting a scene; classify the scene based on the image data; based on the classification of the scene, predict a camera use event; and based on the predicted camera use event, adjust a power mode of at least one of the first image capture device and a second image capture device.

Abstract: Methods, systems, and apparatuses for image segmentation are provided. For example, a computing device may obtain an image, and may apply a process to the image to generate input image feature data and input image segmentation data. Further, the computing device may obtain reference image feature data and reference image classification data for a plurality of reference images. The computing device may generate reference image segmentation data based on the reference image feature data, the reference image classification data, and the input image feature data. The computing device may further blend the input image segmentation data and the reference image segmentation data to generate blended image segmentation data. The computing device may store the blended image segmentation data within a data repository. In some examples, the computing device provides the blended image segmentation data for display.

Abstract: Systems, methods, and non-transitory media are provided for predictive camera initialization. An example method can include obtain, from a first image capture device, image data depicting a scene; classify the scene based on the image data; based on the classification of the scene, predict a camera use event; and based on the predicted camera use event, adjust a power mode of at least one of the first image capture device and a second image capture device.

Abstract: Systems and techniques are provided for processing one or more frames. For example, a process can include obtaining a first plurality of frames associated with a first settings domain from an image capture system, wherein the first plurality of frames is captured prior to obtaining a capture input. The process can include obtaining a reference frame associated with a second settings domain from the image capture system, wherein the reference frame is captured proximate to obtaining the capture input. The process can include obtaining a second plurality of frames associated with the second settings domain from the image capture system, wherein the second plurality of frames is captured after the reference frame. The process can include, based on the reference frame, transforming at least a portion of the first plurality of frames to generate a transformed plurality of frames associated with the second settings domain.

Abstract: Systems and techniques are described for processing image data to generate an image with a synthetic depth of field (DoF). An imaging system receives first image data of a scene captured by a first image sensor. The imaging system receives second image data of the scene captured by a second image sensor. The first image sensor is offset from the second image sensor by an offset distance. The imaging system generates, using at least the first image data and the second image data as inputs to one or more trained machine learning systems, an image having a synthetic depth of field corresponding to a simulated aperture size. The simulated aperture size is associated with the offset distance. The imaging system outputs the image.

Abstract: Systems and techniques are described for imaging. An imaging system includes an image sensor with a plurality of photodetectors, grouped into a first group of photodetectors and a second group of photodetectors. The imaging system can reset its image sensor. The imaging system exposes its image sensor to light from a scene. The plurality of photodetectors convert the light into charge. The imaging system stores analog photodetector signals corresponding to the charge from each the photodetectors. The imaging system reads first digital pixel data from a first subset of the analog photodetector signals corresponding to the first group of photodetectors without reading second digital pixel data from a second subset of the analog photodetector signals corresponding to the second group of photodetectors. The imaging system generates an image of the scene using the first digital pixel data.

Abstract: Systems and techniques are provided for processing one or more frames. For example, a process can include obtaining a first plurality of frames associated with a first settings domain from an image capture system, wherein the first plurality of frames is captured prior to obtaining a capture input. The process can include obtaining a reference frame associated with a second settings domain from the image capture system, wherein the reference frame is captured proximate to obtaining the capture input. The process can include obtaining a second plurality of frames associated with the second settings domain from the image capture system, wherein the second plurality of frames is captured after the reference frame. The process can include, based on the reference frame, transforming at least a portion of the first plurality of frames to generate a transformed plurality of frames associated with the second settings domain.

Abstract: Systems and techniques are described for processing image data to generate an image with a synthetic depth of field (DoF). An imaging system receives first image data of a scene captured by a first image sensor. The imaging system receives second image data of the scene captured by a second image sensor. The first image sensor is offset from the second image sensor by an offset distance. The imaging system generates, using at least the first image data and the second image data as inputs to one or more trained machine learning systems, an image having a synthetic depth of field corresponding to a simulated aperture size. The simulated aperture size is associated with the offset distance. The imaging system outputs the image.