Qualcomm Incorporated

Shizhong Liu

Weiliang Liu

Karthikeyan Shanmugavadivelu

Narayana Karthik Ravirala

Jianfeng Ren

Adarsh Abhay Golikeri

Hung-Hsin Wu

Wan Shun Vincent Ma

Venkata Ravi Kiran Dayana

Narayana Karthik Sadanandam Ravirala

Wei Zou

Lei Ma

Hsiang-Tsun Li

Jiafu Luo

Hau Hwang

Ying Chen Lou

Ron Gaizman

Fan Deng

Hailong Wang

Jincheng Huang

Sahul Madanayakanahalli Phaniraj Venkatesh

Adarsh Hosaagrahara Nagaraja

Nikhil Uchil

Xiaoyun Jiang

Dashan Gao

Suhail Jalil

Xin Zhong

Pengjun Huang

Charles Seunghwan Han

Shang-Chih Chuang

Pradeep Kumar Veeramalla

William Yee-Ming Huang

Ming Yan

Lee-Kang Liu

Tao Ma

Ricky Nguyen

Phan L Do

Srinath Anekere Narayan

Chanchal Raj

Lianming Shi

Kevin Yao Han

Satyajit Giri

Kuan-Chen Pan

Krishnankutty Kolathappilly

Pradeep Veeramalla

Disclosed are systems, apparatuses, processes, and computer-readable media to capture images with subjects at different depths of fields. A method of processing image data includes capturing an image associated with a first exposure; capturing a plurality of images associated with at least a second exposure, the second exposure being less than the first exposure; generating a combined image from the plurality of images associated with at least the second exposure; and generating a high dynamic range (HDR) image from the image associated with the first exposure and the combined image.

High dynamic range (HDR) image generation using a combined short exposure image

Systems and techniques are provided for adjusting objects in images. For example, a process can include obtaining a first image of a scene from a camera. The scene can include a first object positioned at a first position and a second object positioned at a second position. The process can include obtaining a second image of the scene from the camera. The second image of the scene can include the first object positioned at the first position and the second object positioned at a third position different from the second position. The process can include generating an adjusted second image based on the second image. The adjusted second image can include the first object positioned at the first position. The second object at the third position is removed from the adjusted second image. The process can include displaying the adjusted second image on a display.

Removal of objects from images

Disruptions in the continuity of image frames output from an image capture device due to switching from one image sensor to another image sensor of the device may be reduced or eliminated through controlled timing for switching of the image sensors according to a predefined image sensor configuration. Operation of a multi-sensor image device according to the predefined image sensor configuration may include an appropriate selection of a source for image adjustment during the zoom level transition. The predefined image sensor configuration may define transition parameters for particular zoom ranges of the image capture device.

Camera sensor changes in multi-sensor device

Artefacts in a sequence of image frames may be reduced or eliminated through modification of an input image frame to match another image frame in the sequence, such as by geometrically warping to generate a corrected image frame with a field of view matched to another frame in sequence of frames with the image frame. The warping may be performed based on a model generated from data regarding the multi-sensor device. The disparity between image frames may be modeled based on image captures from the first and second image sensor for scenes at varying depths. The model may be used to predict disparity values for captured images, and those predicted disparity values used to reduce artefacts resulting from image sensor switching. The predicted disparity values may be used in image conditions resulting in erroneous actual disparity values.

Transform matrix learning for multi-sensor image capture devices

The present disclosure generally relates to exposure control for image capture devices. One example method generally includes receiving sensor data indicating a movement associated with an image capture device capturing a first frame and a second frame, wherein the first frame is associated with a higher exposure setting as compared to the second frame; predicting, based on the sensor data, a shift for aligning one or more features of the first frame and one or more features of the second frame; applying the shift to align the one or more features of the first frame and the one or more features of second frame; and generating a frame output at least in part by combining at least a portion of the first frame with at least a portion of the second frame.

Methods and systems for shift estimation for one or more output frames

Image frames for computational photography may be corrected, such as through rolling shutter correction (RSC), prior to fusion of the image frames to reduce wobble and jitter artifacts present in a video sequence of HDR-enhanced image frames. First and second motion data regarding motion of the image capture device may be determined for times corresponding to the capturing of the first and second image frames, respectively. The rolling shutter correction (RSC) may be applied to the first and second image frames based on both the first and second motion data. The corrected first and second image frames may then be aligned and fused to obtain a single output image frame with higher dynamic range than either of the first or second image frames.

Image alignment for computational photography

In general, techniques are described regarding initiating a recursive blending of a plurality of frames following a camera transition. One or more processors may, subsequent to a transition from a first camera mode to a second camera mode, blend one or more pixels of a first frame with one or more pixels of a second frame to produce a first blended frame, the first frame captured via a first camera, and the second frame captured via a second camera. The one or more processors may then output the first blended frame.

Camera transition blending

This disclosure provides devices, methods, computer-readable medium, and means for spatial alignment transform. In some aspects, a device may perform operations including capturing a first image of a scene using a first sensor at a first zoom ratio, capturing a second image of the scene using a second sensor at a second zoom ratio, the first image having a different field-of-view (FOV) than the second image, determining a translation matrix based on one or more spatial misalignments between the first image and the second image, determining a confidence associated with the translation matrix, in response to the confidence being greater than a confidence threshold, determining a weighting factor based on the first zoom ratio, the second zoom ratio, and a current zoom ratio of the device, applying the weighting factor to the translation matrix, and warping the first image to the second image using the weighted translation matrix.

Spatial alignment transform without FOV loss

In general, techniques are described regarding adaptation of image capture modes to provide refined image capture capabilities. Cameras comprising image sensors configured to perform the techniques are also disclosed. An image sensor may use different image capture modes to capture frames of image data based at least in part on various operating conditions. For example, the processor can cause an image sensor to transition between image capture modes specifying different binning levels in the case that a digital zoom level satisfies a predefined threshold.

Image capture mode adaptation

Methods, systems, and apparatuses are provided that adapt image capture operations to provide refined image capture capabilities. For example, an image capture device captures image data from a sensor operating at a first binning level. The image capture device receives a desired level of zoom, and determines whether the sensor is to be transitioned from the first binning level to a second binning level based on a binning transition threshold. If the sensor is to be transitioned, the image capture device adjusts one or more image capture operations, such as auto focus, automatic exposure, automatic gain, or automatic white balance, based on the desired level of zoom. After an amount of time, the image capture device transitions the sensor from the first binning level to the second binning level. The image capture device then captures image data from the sensor operating at the second binning level.

Apparatus and methods for image capture control

Systems, methods, and computer-readable media are provided for automated slow motion video recording. An example method can include storing, in a buffer, a sequence of ordered frames captured by an image sensor; sending, for display, a first frame from the sequence of ordered frames in the buffer at a time interval; calculating an amount of motion between the first frame in the buffer and a second frame in the buffer; determining, based on the amount of motion, a frame rate for encoding a set of frames from the sequence of ordered frames in the buffer, the set of frames including the second frame in the buffer and one or more frames sequentially between the first frame and the second frame; and encoding the set of frames at the frame rate determined.

Automatic slow motion video recording

Systems and methods for synchronizing frame captures for cameras with different fields of capture are described. An example device includes a first camera and second camera. The first camera includes a first camera sensor and a first rolling shutter. The first camera is configured to prevent scanning pixels from a first row to a row n of the first camera sensor and configured to begin sequentially scanning pixels of the row n of the first camera sensor. The second camera includes a second camera sensor and a second rolling shutter. The second camera is configured to begin sequentially scanning pixels of a first row of the second camera sensor concurrently with beginning to sequentially scan pixels of the row n of the first camera sensor. The first row of the second camera sensor corresponds to a row within a predefined number of rows after the first camera sensor's row n.

Synchronization of frame captures from multiple cameras with different fields of capture

Method and systems for autofocus triggering focusing are disclosed herein. In one example, a system may include a lens, a memory component configured to store lens parameters of the lens and regions of focus corresponding to the lens parameters, and a processor coupled to the memory and the lens. The processor may be configured to focus the lens on a target object at a first instance of time, receive information indicative of distances from an imaging device to the target object over a period of time, obtain lens parameters of the lens, and determine a region of focus, and trigger the lens to re-focus on the target object if the distance to the target object indicates the target object is outside of the region of focus and the distance to the target object is unchanged for a designated time period.

Systems and methods for autofocus trigger

Systems, devices, and methods of displaying and/or recording multiple pictures in a picture (PIP) on the same display of a digital display device are disclosed. The PIPs can show objects from the main field view of the display device, such as a front camera lens, as well as objects from a different field of view, such as a back camera lens. The PIPs can further track the objects that are being displayed.

Systems, devices and methods for displaying pictures in a picture

A method for generating image calibration data at an electronic device having multiple cameras includes capturing a first image of a first scene at a first time using a first camera at the electronic device. The method also includes capturing a second image of a second scene at the first time using a second camera at the electronic device. A portion of the first scene overlaps a portion of the second scene. The method further includes generating the image calibration data based on the first image and based on the second image.

Real time calibration for multi-camera wireless device

Growing community of inventors

San Diego, CA, United States of America

Shizhong Liu