Multi-Sensor Exposure Prioritization for Seamless Composite Imaging
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Solution Overview
Problem
Conventional image capture systems face challenges in producing high-quality spherical images due to separate auto exposure processing for rear and front image sensors, leading to visible exposure variations and poor dynamic range along the stitch line, especially when scene brightness levels differ.
Innovation Solution
Implement sensor prioritization by determining image processing parameters based on a prioritized sensor, applying these parameters to both images, and updating luminance values to produce a composite image, such as a spherical image, thereby minimizing exposure variations and enhancing image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate auto exposure processing is applied to rear and front image sensors, then each sensor can be optimized for its own hemisphere, but local exposure variations and visible discontinuities occur at the stitch line
Solution Approach 1:
The patent merges the auto exposure processing for multiple image sensors by determining a single set of image processing parameters that applies uniformly across all sensors. This is achieved by selecting a prioritized sensor and using its image data to determine parameters that are then applied to all other sensors, ensuring consistent exposure across the composite spherical image while eliminating stitch line discontinuities.
2Loss of information
If image processing parameters are determined using data from all image sensors, then comprehensive scene information is available, but computational complexity increases
Solution Approach 1:
The patent extracts only the necessary information for parameter determination from a single prioritized sensor, rather than processing data from all sensors. By selecting one sensor as prioritized and using only its image data to determine auto exposure, auto white balance, and tone mapping parameters, the system significantly reduces computational complexity while still capturing comprehensive scene information through the composite image of all sensors.
3Manufacturing precision
If different image processing parameters are applied to different hemispheres, then local image quality is optimized, but discontinuities appear at the stitching boundary
Solution Approach 1:
The patent applies homogeneity by using the same image processing parameters for all image sensors in the array. Instead of optimizing parameters separately for each hemisphere or sensor, the system determines a single set of parameters based on the prioritized sensor and applies them uniformly to all sensors, ensuring consistent image quality across the entire composite image while eliminating visible discontinuities at stitch lines.
Data Source
AI summary
Systems and methods are disclosed for sensor prioritization for composite image capture. For example, methods may include selecting an image sensor as a prioritized sensor from among an array of two or more image sensors; determining one or more image processing parameters based on one or more images captured using the prioritized sensor; applying image processing using the one or more image processing parameters to images captured with each image sensor in the array of two or more image sensors to obtain respective processed images for the array of two or more image sensors; and stitching the respective processed images for the array of two or more image sensors to obtain a composite image.