Camera Color Mask Segmentation for High-Sensitivity Night Vision
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Solution Overview
Problem
Color cameras used in vehicles face a trade-off between color differentiation and high-sensitivity resolution, with standard Bayer patterns reducing sensitivity by half, and existing methods struggle to accurately detect and differentiate red and white light sources, especially in night vision applications.
Innovation Solution
Deriving additional color data from intensity values and existing color values, using a color filter that attenuates non-red spectral light, allowing for the estimation of missing color proportions, such as turquoise, to enhance color differentiation without sacrificing sensitivity, and creating a higher-dimensional color space for improved image processing and representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a standard Bayer pattern color mask is used, then color differentiation is improved, but high-sensitivity resolution deteriorates by a factor of 2
Solution Approach 1:
The color mask is segmented into different functional zones: colored filter pixels (red, green, blue) for color differentiation and transparent filter pixels for high-sensitivity intensity measurement. This segmentation allows each pixel type to specialize in its function while collectively providing both color and sensitivity data.
Solution Approach 2:
The patent transitions from a 2D Bayer pattern to a 3D color space by combining intensity values (from transparent pixels) with color values (from colored pixels) to reconstruct complete RGB color information. This dimensional approach allows recovery of color data that would otherwise be lost in reduced filter masks.
2Manufacturing precision
If reduced filter masks with omitted color pixels are used, then high-sensitivity resolution is improved, but color data completeness deteriorates
Solution Approach 1:
The evaluation device uses feedback from intensity pixels to compensate for missing color pixel data. By analyzing the intensity values and using the available color pixel information, the system reconstructs the complete color spectrum through computational processing, effectively feeding back color information that would otherwise be absent.
Solution Approach 2:
The transparent filter pixels act as intermediaries that capture full-spectrum intensity information, which then serves as a basis for reconstructing the complete color data. These intensity values mediate between the limited color pixel measurements and the full color spectrum, enabling recovery of missing color information.
3Loss of information
If color masks with multiple color filter pixels are used, then color data completeness is improved, but device complexity increases
Solution Approach 1:
The transparent filter pixels serve multiple functions: they provide high-sensitivity intensity measurement and simultaneously serve as a basis for reconstructing complete color information through their full-spectrum sensitivity. This multi-functionality reduces the need for dedicated pixels of each color type.
Solution Approach 2:
The patent changes the parameters of the filter mask by using transparent pixels with broad spectral sensitivity instead of requiring complete coverage of all color wavelengths through colored filters. This parameter change in filter transparency enables computational reconstruction of color data with reduced hardware complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables effective detection and differentiation of traffic signs, lights, and objects, maintaining high sensitivity for night vision while providing enhanced color data for improved image representation and processing, with results comparable to monochromic cameras in terms of spatial resolution.
Implementation Method 1
A colored filter pixel of the color mask leads to an attenuation of the spectral light proportion of the other color contents.
Implementation Method 2
sensors are generally used having a color mask which makes possible a color differentiation
Data Source
AI summary
A method for ascertaining image signals having color values and a camera set-up, which has: a camera having camera optics, an image sensor for recording an environment and for outputting first image signals, and a color mask applied in front of the image sensor, and an evaluation device, which picks up the first image signals emitted by the image sensor, the image sensor having a plurality of sensor pixels and the color mask having a plurality of filter pixels which are each situated in front of the sensor pixels and include first colored filter pixels and transparent filter pixels; and some of the sensor pixels picking up the light via the colored filter pixels and outputting the first color values, and additional sensor pixels picking up the light via the transparent filter pixels and outputting the intensity values. The evaluation device ascertains second color values from the intensity values and the first color values of various sensor pixels, and forms second image signals from the first color values and the second color values.