Color Correction Matrix Noise Reduction via Segmentation
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Solution Overview
Problem
Existing color correction methods in image capture systems amplify noise due to large absolute values in color correction matrix elements, leading to error amplification and increased noise in images.
Innovation Solution
Separating the color correction matrix into two parts, where one part (C1) is a unity matrix for luminance information and the other (C2) is a zero-mean matrix for chrominance information, applying C1 to the original pixel stream and C2 to a low-pass filtered version, and combining the results to minimize noise amplification while retaining sharpness perception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a standard color correction matrix is applied to correct color in captured images, then color accuracy is improved, but noise is amplified due to large absolute values in matrix elements
Solution Approach 1:
The color correction matrix is segmented into two separate matrices: a luminance correction matrix that preserves sharpness and a chrominance correction matrix that is low-pass filtered. This segmentation allows each matrix to be optimized for its specific function, reducing overall noise amplification while maintaining color accuracy.
Solution Approach 2:
Different quality requirements are applied to different components of the image: luminance information is processed with a unity matrix to preserve sharpness and detail, while chrominance information is processed with a low-pass filtered matrix to reduce noise. This local quality approach ensures that each component receives appropriate processing tailored to its characteristics.
2Reliability
If color correction matrix elements with large absolute values are used to correct for non-ideal color separation, then color correction effectiveness is improved, but error amplification increases leading to degraded image quality
Solution Approach 1:
The correction matrix is divided into luminance and chrominance components, allowing the chrominance portion to use a low-pass filtered version that reduces error amplification while the luminance portion maintains the necessary correction strength without excessive noise amplification.
Solution Approach 2:
The chrominance correction matrix is pre-filtered with a low-pass filter before being applied to the image data. This preliminary filtering action removes high-frequency noise components that would otherwise be amplified by the correction process, preventing error amplification before it occurs.
3Object-generated harmful factors
If noise reduction filtering is applied to the entire image, then noise is reduced, but sharpness and detail are lost
Solution Approach 1:
The image processing is segmented into luminance and chrominance channels, with the low-pass filtering applied only to the chrominance correction matrix. This selective filtering reduces noise in the color information while preserving the sharpness and detail in the luminance information, avoiding the trade-off that would occur with full-image filtering.
Solution Approach 2:
Different processing qualities are applied to different image components: the luminance channel maintains high-frequency detail and sharpness, while the chrominance channel receives low-pass filtering for noise reduction. This local quality differentiation allows noise reduction without sacrificing overall image sharpness.
Data Source
AI summary
An image processing system includes a filtering arrangement configured to receive incoming pixel information and filter at least a first portion of the information to thereby pass a second portion of the information for further processing; circuitry configured to apply a first color correction function to the incoming pixel information to thereby produce a modified first portion; circuitry configured to apply a second color correction function to the second portion to thereby produce a modified second portion; and an adder configured to combine the modified first portion to the modified second portion.