Sparse Blue Sampling for Color Filter Arrays
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Solution Overview
Problem
Current imaging systems, particularly those using RGBW color filter arrays, suffer from color aliasing due to sparse sampling of the Blue color plane, which affects image quality and compression efficiency, and struggle with chromatic aberration, leading to misalignment and focusing errors.
Innovation Solution
The method involves sparse sampling of the Blue color plane and reconstructing missing samples using a guide image or colorization algorithms, transforming color planes into luminance and chrominance, and compressing these planes to correct chromatic aberration by relocating and upsampling Blue samples aligned with other color planes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If RGBW color filter arrays are used to capture 4 colors, then light sensitivity is improved, but color aliasing occurs due to sparse sampling of the Blue color plane
Solution Approach 1:
The patent segments the color sampling process by treating the Blue color plane separately from other color planes. It identifies and extracts only the necessary Blue samples from the sparsely sampled data, rather than attempting to uniformly sample all color planes. This segmentation allows the system to work with the limited Blue samples available while maintaining overall color accuracy through separate processing of the Blue channel.
2Device complexity
If sparse sampling of Blue color plane is performed, then device complexity is reduced, but color aliasing and image quality degradation occur
Solution Approach 1:
The patent introduces an intermediary computational process that acts as a mediator between the sparsely sampled Blue color plane and the final reconstructed image. This intermediary step involves algorithms that infer and reconstruct the missing Blue samples by leveraging correlations with other color planes and spatial information, thereby bridging the gap between sparse input data and complete color information without requiring additional physical sensors.
3Manufacturing precision
If chromatic aberration correction is applied by relocating Blue samples, then alignment accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent applies preliminary action by performing chromatic aberration correction and Blue sample relocation during the initial image processing stage, rather than as a subsequent correction step. By integrating the relocation operation into the fundamental sampling and reconstruction process, the system establishes proper alignment early in the pipeline, which prevents compounding errors and reduces the need for iterative corrections later, thereby reducing overall processing time despite the additional computational step.
Data Source
AI summary
A method for coding color images with fewer blue samples than samples of other colors. This provides a psycho visually high image quality since the human retina itself has fewer S cones than L, M cones. Applications include image and video coding with lower density of blue samples than other colors such as red and green. Another application is in single sensor multi-spectral and color cameras that use Color Filter Arrays. Sampling density is limited in Color Filter Arrays so that a lower blue sample density enables higher sample densities of other colors.