Bayer Filter Image Brightness Compensation Using Matrix Blending
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Solution Overview
Problem
Bayer color filters, used in image sensing, can improve signal-to-noise ratio but introduce color compensation bias and image brightness distortion when capturing images in dark scenes, especially when detecting primary colors and infrared light, leading to compromised image resolution and fidelity.
Innovation Solution
An image color brightness compensation method and system that uses a Bayer color filter, demosaic process, and linear combination with matrices to generate and blend image data, including red, green, blue, and infrared light data, to adjust brightness through a blending unit and brightness compensation unit, ensuring accurate color and brightness compensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a Bayer color filter is used to improve signal-to-noise ratio in dark scenes, then the SNR is improved, but color fidelity and image resolution are compromised due to infrared light impact
Solution Approach 1:
The patent segments the image processing into multiple stages: initial Bayer filter capture, demosaic processing, matrix-based color space transformation, and separate brightness compensation. Each stage handles specific aspects of image data independently, allowing optimized processing for both SNR and color fidelity without mutual interference.
Solution Approach 2:
The patent applies different processing qualities to different components of the image data. The brightness compensation is applied selectively to maintain color fidelity while improving overall image quality. The matrix transformation uses different coefficients for different color channels (R, G, B, IR) to preserve local color characteristics while compensating for infrared light impact.
2Measurement precision
If a calibration process is introduced to correct color offsets after Bayer filter processing, then color fidelity is improved, but color compensation bias and image brightness distortion are introduced
Solution Approach 1:
The patent performs brightness compensation as a preliminary action before final color calibration. By compensating for brightness differences caused by the Bayer filter pattern early in the processing chain, subsequent color calibration operations work with already-corrected brightness data, preventing the propagation of brightness distortion through the calibration process.
Solution Approach 2:
The patent introduces an intermediary matrix transformation step between the Bayer filter output and the final color calibration. This matrix operation serves as a mediator that transforms the image data into an intermediate color space where brightness and color corrections can be applied independently, preventing the interference that causes compensation bias.
3Quantity of substance
If infrared light detection is added to the light sensor along with three primary colors, then more light information is captured, but image resolution is sacrificed due to the need for calibration
Solution Approach 1:
The patent makes the image processing system universal by handling multiple light types (R, G, B, IR) through a unified matrix transformation approach. The same processing pipeline and compensation algorithms work for all color channels simultaneously, eliminating the need for separate calibration processes for each channel and preserving resolution while capturing comprehensive light information.
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
The method effectively compensates image brightness and color, maintaining true and natural colors while optimizing image quality by using customized matrices for different brightness intervals and avoiding brightness distortion, thus enhancing image fidelity and resolution.
Implementation Method 1
a Bayer color filter of an image sensing unit... The image data of the plurality of pixels, the demosaic image data, the output image data, the blended image data, and the brightness compensated image data comprise red light data, green light data, blue light data, and infrared light data
Data Source
AI summary
An image color brightness compensation method includes generating image data of a plurality of pixels by using a Bayer color filter of an image sensing unit, generating demosaic image data after the image data of the plurality of pixels is processed by a demosaic process, executing a linear combination process of the demosaic image data by using at least one matrix for generating output image data, processing the output image data by using a blending unit for generating blended image data, generating a brightness compensation gain of the blended image data, and compensating image brightness of the blending image data for generating brightness compensated image data according to the brightness compensation gain.