Qualcomm Incorporated

Weiliang Liu

Shizhong Liu

Jianfeng Ren

Narayana Karthik Ravirala

Wei Zou

Xiaoyun Jiang

Ron Gaizman

Sahul Madanayakanahalli Phaniraj Venkatesh

Jincheng Huang

Adarsh Hosaagrahara Nagaraja

Nikhil Uchil

Jiafu Luo

Weihua Xiong

Shang-Chih Chuang

Pradeep Kumar Veeramalla

Lee-Kang Liu

Hailong Wang

Xianbiao Shu

Tao Ma

Srinath Anekere Narayan

Chanchal Raj

Lianming Shi

Kevin Yao Han

Satyajit Giri

Kuan-Chen Pan

Krishnankutty Kolathappilly

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

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

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

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

Systems and methods for multi-focus imaging are disclosed. An imaging system may suggest multiple regions of interest to a user based on a preview mode of a scene of interest. The user may select a plurality of the suggested regions of interest for a multi-focus image. The imaging system calculates the autofocus parameters for each of the multiple regions of interest and captures single images of each of the regions of interest when the shutter button of the camera is pressed. The imaging system then generates a multi-focus image from the multiple images.

System and method for multi-focus imaging

Certain aspects relate to systems and techniques for correction of color artifacts including both color aberration and color spot artifacts using a unified framework. For example, both color aberration and color spot artifacts can be corrected using a post-processing method implementing directional median filtering on chroma channels. A pixel-by-pixel correction ratio map can be built by analyzing the luma and chroma components of the image to indicate a type of color artifact associated with each pixel in the image, and a directional median filter can be selected for each pixel based on the corresponding correction ratio map value.

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San Diego, CA, United States of America

Weiliang Liu