Qualcomm Incorporated

Ron Gaizman

Meir Tzur

Eran Pinhasov

Hee Jun Park

Shizhong Liu

Tauseef Kazi

Young Hoon Kang

Weiliang Liu

Narayana Karthik Ravirala

Eliad Tsairi

Xiaoyun Jiang

Victor Pinto

Jiafu Luo

Nir Strauss

Shang-Chih Chuang

Pradeep Kumar Veeramalla

Yuri Dolgin

Wei Zou

Mark Gintsburg

Maksym Aslianskyi

Srinath Anekere Narayan

Chanchal Raj

Lianming Shi

This disclosure provides systems, methods, and devices for image processing that support improved image quality from reduced artifacts and wobbles. In a first aspect, a method of image processing includes receiving motion data regarding movement of a mobile device; determining a first image sensor configuration based on the motion data; and configuring an image sensor with the first image sensor configuration. Other aspects and features are also claimed and described.

Dynamic image capture device configuration for improved image stabilization

Techniques and systems are provided for processing one or more frames or images. For instance, a process for determining exposure for one or more frames includes obtaining a motion map for one or more frames. The process includes determining, based on the motion map, motion associated with the one or more frames of a scene. The motion corresponds to movement of one or more objects in the scene relative to a camera used to capture the one or more frames. The process includes determining, based on the determined motion, a number of frames and an exposure for capturing the number of frames. The process further includes sending a request to capture the number of frames using the determined exposure duration.

Frame processing and/or capture instruction systems and techniques

Example are described for combining optical image stabilization and electronic image stabilization for capture and processing of frames for video. An example device configured to perform electronic image stabilization in light of optical image stabilization performed may include a memory and one or more processors. The one or more processors may be configured to obtain optical image stabilization (OIS) information for OIS performed during capture of a sequence of frames by an image sensor and determine an electronic image stabilization (EIS) filter based on the OIS information. The one or more processors may also be configured to obtain camera position information, and the EIS filter may also be based on the camera position information. The one or more processors may also configure an image signal processor to perform EIS based on the EIS filter.

Combined electronic image stabilization and optical image stabilization

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

Techniques and systems are provided for machine-learning based image stabilization. In some examples, a system obtains a sequence of frames captured by an image capture device during a period of time, and collects motion sensor measurements calculated by a motion sensor associated with the image capture device based on movement of the image capture device during the period of time. The system generates, using a deep learning network and the motion sensor measurements, parameters for counteracting motions in one or more frames in the sequence of frames, the motions resulting from the movement of the image capture device during the period of time. The system then adjusts the one or more frames in the sequence of frames according to the parameters to generate one or more adjusted frames having a reduction in at least some of the motions in the one or more frames.

Image stabilization using machine learning

Growing community of inventors

Haifa, Israel

Ron Gaizman