Digital Image Color Processing Reducing Noise Amplification
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Solution Overview
Problem
Conventional color processing methods in digital imaging devices amplify noise, particularly in small-gamut sensors, leading to a degradation of image quality in the desired color space.
Innovation Solution
A method involving low-pass and high-pass filtering of digital images to obtain respective components, followed by edge detection to determine edginess parameters, which are then used to adaptively perform color-space transformation, reducing noise amplification by selectively applying color matrix operations based on the total information from these components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional color-space transformation is applied to transform device RGB space to standard color space, then color accuracy is improved, but noise is amplified significantly
Solution Approach 1:
The image is segmented into low-frequency components (smooth regions) and high-frequency components (edge regions) using Gaussian filtering. Different color transformation strategies are applied to each segment: standard matrix transformation for low-frequency regions and identity transformation for high-frequency regions, thereby avoiding noise amplification while maintaining color accuracy.
Solution Approach 2:
The invention applies different processing quality to different regions of the image based on their frequency characteristics. Low-frequency regions receive full color transformation processing, while high-frequency regions are preserved with minimal processing to avoid noise amplification, achieving local optimization of both color accuracy and noise control.
2Reliability
If color matrix operations are applied to achieve white balancing and color-space transform, then color reproduction is improved, but signal-to-noise ratio is significantly reduced
Solution Approach 1:
The invention dynamically adjusts the color transformation process based on the frequency content of different image regions. The processing intensity varies dynamically: full transformation for low-frequency components and reduced transformation for high-frequency components, optimizing both color reproduction and signal-to-noise ratio adaptively.
Solution Approach 2:
The invention changes the transformation parameters based on frequency domain analysis. By decomposing the image into different frequency components and applying appropriate transformation matrices to each, the parameters are optimized to maintain color reproduction while minimizing noise amplification in different regions.
Data Source
AI summary
An embodiment relates to a method for color processing of an input image, the method including the steps of low-pass filtering of the input image to obtain a low-pass component, high-pass filtering of the input image to obtain a high-pass component, processing the input image for edge detection to obtain edginess parameters, and performing a color-space transformation of the input image based on the low-pass component, the high-pass component, and the edginess parameters.