Adaptive Bayer Pattern for Depth Extraction
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Solution Overview
Problem
Existing camera lens array technologies fail to provide a power-efficient and cost-friendly method for discerning depth from images, as they are mostly focused on super resolution techniques that are computationally taxing and do not effectively utilize depth information.
Innovation Solution
A method to generate an adaptive Bayer pattern by rearranging monochromatic pixel elements into a synthetic pattern, allowing for depth information extraction and demosaicing without relying on a fixed pixel-wise element set, using a dual-pair of front-facing cameras with varying resolutions to create a touchless gestural interface and enhance image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If super resolution techniques are used to discern depth from camera lens array images, then depth information can be extracted, but computational complexity and power consumption increase significantly
Solution Approach 1:
The patent extracts only the necessary depth information from the image data using simplified algorithms that focus on specific features (such as disparity between lens array elements) rather than performing full super resolution processing. This selective extraction reduces computational load and power consumption while still providing usable depth data for applications like gesture recognition.
Solution Approach 2:
The patent changes the processing parameters by using lower computational complexity algorithms that trade off some depth measurement precision for significantly reduced power consumption. This is achieved by using simpler disparity calculation methods and avoiding the computationally intensive super resolution techniques that would otherwise be required.
2Measurement precision
If super resolution techniques are used to discern depth from camera lens array images, then depth information can be extracted, but computational complexity increases
Solution Approach 1:
The patent extracts only the necessary depth information from the image data using simplified algorithms that focus on specific features (such as disparity between lens array elements) rather than performing full super resolution processing. This selective extraction reduces computational load and power consumption while still providing usable depth data for applications like gesture recognition.
Solution Approach 2:
The patent changes the processing parameters by using lower computational complexity algorithms that trade off some depth measurement precision for significantly reduced power consumption. This is achieved by using simpler disparity calculation methods and avoiding the computationally intensive super resolution techniques that would otherwise be required.
3Device complexity
If a fixed pixel-wise element set is used for image processing, then processing is simpler, but adaptability to different depth scenarios is reduced
Solution Approach 1:
The patent implements a dynamic processing approach where the pixel-wise element set and processing parameters are adjusted based on the detected depth scenarios. The system adapts its processing strategy in real-time, selecting different demosaicing and depth extraction methods appropriate for the current scene conditions, thereby achieving both simplicity and adaptability.
Solution Approach 2:
The patent dynamically changes processing parameters including the pixel-wise element set configuration based on the detected depth and scene characteristics. This allows the system to optimize its processing approach for different scenarios (such as close-up vs. distant objects) without requiring a completely complex fixed architecture.
4Manufacturing precision
If high-resolution grids are used for image processing, then image quality improves, but computational requirements and power consumption increase
Solution Approach 1:
The patent applies partial action by processing only the necessary regions of the image at high resolution, rather than the entire image. By using the depth information from the lens array to identify regions of interest, the system applies high-resolution processing selectively to those areas while using lower resolution processing for the rest, thereby maintaining image quality where needed while reducing overall power consumption.
Data Source
AI summary
A method and apparatus for creating an adaptive mosaic pixel-wise virtual Bayer pattern. The method may include receiving a plurality of monochromatic images from an array of imaging elements, creating a reference ordered set at infinity from the plurality of monochromatic images, running a demosaicing process on the reference ordered set, and creating a color image from the demosaiced ordered set. One or more offset artifacts resulting from the demosaicing process may be computed at a distance other than infinity, the ordered set may be modified in accordance with the computed offsets.