Bayer Image Frequency Decomposition for Reduced Data Processing
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Solution Overview
Problem
Existing image processing methods require interpolation of pixel components to differentiate between chroma and brightness components, leading to increased data processing demands and memory requirements.
Innovation Solution
An image processing device and method that decompose a Bayer image into low and high frequency components, allowing for distinct processing details based on the type of component, thereby reducing data processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If interpolation process is performed on Bayer image to form RGB image, then chroma and brightness components can be differentiated for separate processing, but the amount of data processed increases and memory capacity requirements increase
Solution Approach 1:
The patent applies segmentation by performing frequency decomposition on the Bayer image to separate it into multiple subimages with different frequency characteristics. The low-frequency subimage contains brightness information while high-frequency subimages contain chroma information. This segmentation allows separate processing of chroma and brightness components without requiring full interpolation to RGB format, thus reducing data amount while maintaining processing precision.
Solution Approach 2:
The patent extracts specific frequency components from the Bayer image through frequency decomposition. By extracting only the necessary frequency subimages (low-frequency for brightness, high-frequency for chroma) and processing only those, the method avoids processing the entire interpolated RGB image data, thereby reducing memory requirements and data processing load while achieving differentiated chroma and brightness processing.
2Manufacturing precision
If interpolation process is performed on Bayer image to form RGB image, then chroma and brightness components can be differentiated for separate processing, but processing time increases
Solution Approach 1:
The patent segments the image processing task by decomposing the Bayer image into frequency subimages and processing only the necessary components separately. Instead of performing time-consuming interpolation to create a full RGB image and then processing all data, the method processes only the extracted low-frequency subimage for brightness and high-frequency subimages for chroma, significantly reducing processing time while maintaining precision.
Solution Approach 2:
The patent performs preliminary frequency decomposition on the Bayer image to extract the necessary frequency subimages before processing. This preliminary action identifies and separates the brightness and chroma components in advance, allowing subsequent processing to focus only on the relevant data rather than processing the entire interpolated image, thus reducing overall processing time.
3Manufacturing precision
If memory capacity is increased to handle larger data from interpolated images, then separate processing of chroma and brightness components is enabled, but device complexity increases
Solution Approach 1:
The patent segments the image data into frequency subimages through decomposition, allowing processing of smaller, separated data sets rather than requiring large memory capacity for full interpolated RGB images. This segmentation enables separate chroma and brightness processing with reduced memory requirements, avoiding increased device complexity while maintaining processing precision.
Solution Approach 2:
The patent extracts only the necessary frequency components (low-frequency for brightness, high-frequency for chroma) from the Bayer image, avoiding the need to store and process the entire interpolated RGB image data. This extraction approach enables differentiated processing with smaller memory capacity, preventing device complexity increases.
Data Source
AI summary
Image processing is performed in view of the difference between the chroma component and the brightness component in an image, with a relatively smaller amount of data processed during the image processing. A frequency decomposing unit 110 performs frequency decomposition directly on Bayer image signals from an imaging element 3. With this, a high frequency component representing color information and a low frequency component representing brightness information are obtained. A filter coefficient obtaining unit 131 of a correction processing unit 130 obtains filter coefficients and the filter coefficients for the high frequency component are different from those for the low frequency component. A filtering processing unit performs a filtering process on subimages based on the filter coefficients obtained by the filter coefficient obtaining unit 131 in such a manner that processing details of the filtering process for the high frequency component are different from those for the low frequency component.