Qualcomm Incorporated

Hui Shan Kao

Wen-Chun Feng

Hsuan-Ming Liu

Jian-Jia Su

Hang-Wei Liaw

Micha Galor Gluskin

Kai Yin Liu

Yawen Chi

The present disclosure provides systems, apparatus, methods, and computer-readable media that support improved image signal processing, particularly in low-light or high dynamic range (HDR) scenes. The image processing techniques may include performing two automatic exposure control (AEC) operations, in which a first AEC operation targets obtaining good conditions for autofocus (AF) operation, and a second AEC operation follows the AF operation and targets obtaining good conditions for an image capture. The image processing techniques may also include communication between the AEC operations and AF operations to coordinate the operations by locking and releasing exposure levels and focus positions as part of the image signal processing. In one aspect, a processing technique may include an AF measuring statistics from target, coordinating with AEC, and performing interleaving of a lock-and-resume state machine to achieve better AF result in challenging low-light or HDR scenes while maintaining an overall image quality.

Autofocus (AF) and auto exposure control (AEC) coordination

This disclosure provides systems, methods, and devices for image capture and image processing that support capture using a variable aperture (VA) camera. In a first aspect, a method of image processing includes determining a plurality of depths for a scene, the plurality of depths corresponding to a plurality of regions of interest; determining an aperture size for a camera based on the plurality of depths; determining a lens position for the camera based on the aperture size and the plurality of depths; and controlling the camera to adjust a variable aperture (VA) of the camera based on the aperture size and to adjust a lens associated with the variable aperture (VA) based on the lens position. Other aspects and features are also claimed and described.

Variable aperture (VA) camera control for controlling a depth of focus

This disclosure provides systems, methods, and devices for image capture using an autofocus (AF) algorithm. In a first aspect, a method for autofocus includes receiving first image data of a first scene from a first image sensor of a first camera, the first image data comprising phase shift information; determining a first focal distance for the first scene based on a machine learning algorithm by inputting the phase shift information to the machine learning algorithm; and controlling a focal position of the first camera based on the first focal distance. Other aspects and features are also claimed and described.

Machine learning for phase detection autofocus

This disclosure provides systems, methods, and devices for image processing that support enhanced image effects, such as bokeh effect, applied in image processing. In a first aspect, a method of image processing includes determining a depth map corresponding to the first scene based on first image data and second image data captured at different aperture sizes; determining a focus map based on the depth map and a simulated aperture size different from the first aperture size and the second aperture size; and determining an output image frame based on the focus map, the first image data, and the second image data. Other aspects and features are also claimed and described.

Bokeh effect in variable aperture (VA) camera systems

An apparatus configured for image processing comprises one or more processors configured to determine data associated with a distance between an object and the apparatus and determine a plurality of lens positions of a camera lens based on the data associated with the distance between the object and the apparatus. The one or more processors are further configured to determine, for each one of the plurality of lens positions, a respective focus value to generate a plurality of focus values. To determine, for each one of the plurality of lens positions, the respective focus value, the one or more processors are configured to determine, for each one of the plurality of lens positions, phase difference information. The one or more processors are further configured to determine a final lens position based on the plurality of focus values.

Depth-assisted auto focus

Methods, systems, and devices for feature-based image autofocus are described. A device may perform an autofocus procedure that includes determining a set of features based on determining a feature region associated with an image. The device may generate a feature weight map based on the set of features and estimate a direction of a target feature in the feature region. The device may generate a direction weight map that corresponds to the feature region. The device may determine a focus position of the image based on the generated feature weight map and the estimated direction of the target feature and perform an autofocus operation on the determined focus position of the image. The device may calculate a focus value based on the feature weight map, and the focus position of the image may be based on the focus value.

Feature-based image autofocus

An image processing device receives image data from an image frame captured by an image sensor of an image capture device. The image processing device moves a lens of the image capture device while still receiving the image data from the image frame, for instance as part of an autofocus operation. The image processing device determines multiple positions of the lens from the beginning of the movement to the end of the movement based on a lens position sensor, based on differences between the image frame and other consecutive image frames, or based on interpolation. The image processing device performs field of view compensation on the image data by cropping one or more rows of the image data based on the positions of the lens.

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Taipei, Taiwan

Hui Shan Kao