Image Recovery Filter Segmentation for Bayer RAW Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Image recovery processing often generates false colors and increases processing load due to differences in frequency characteristics between color components in RAW images, particularly when applying image recovery filters to images with Bayer arrangements.
Innovation Solution
The solution involves separating the green (G) component into G1 and G2 components, allowing for image recovery processing within a common frequency band, thereby reducing false colors and processing load by using image recovery filters tailored to each color component, including R, G1, G2, and B, which have matching spatial frequency characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image recovery processing is applied to RAW images with Bayer arrangements, then image quality improvement is achieved, but false colors are generated due to frequency characteristic differences between color components
Solution Approach 1:
The green component pixels in the Bayer arrangement are divided into two separate groups: G1 pixels (surrounded by R pixels) and G2 pixels (surrounded by B pixels). This segmentation allows each group to be processed with appropriate image recovery filters matched to their specific frequency characteristics, preventing false color generation while maintaining image quality improvement.
2Manufacturing precision
If image recovery processing is applied to RAW images with Bayer arrangements, then image quality improvement is achieved, but processing load increases due to frequency characteristic differences between color components
Solution Approach 1:
By segmenting the green component into G1 and G2 groups and applying dedicated image recovery filters to each, the processing becomes more efficient. Each filter can be optimized for its specific frequency characteristics, reducing computational complexity compared to applying a single generic filter to all color components.
Solution Approach 2:
Different image recovery filters are applied to different color component groups based on their local frequency characteristics. The G1 component uses a filter matched to its R-adjacent characteristics, while G2 uses a filter matched to its B-adjacent characteristics, achieving optimal processing efficiency for each local region.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An image processing apparatus which performs recovery processing of correcting a degradation in image due to an aberration in an optical imaging system, with respect to image data of a plurality of colors, characterized by comprising: separation means for separating image data into image data of the respective colors, and further separating image data of a color whose spatial frequency characteristic is higher than that of another color due to an arrangement of color filters of the plurality of colors into a plurality of image data of same color so as to have the same spatial frequency characteristic as that of the other color; a plurality of image processing means for performing recovery processing by filter processing for each of the separated image data; and interpolation processing means for performing color interpolation processing of each pixel for the image data having undergone the recovery processing.