Image Processing Apparatus Reducing Color Noise and False Color
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Solution Overview
Problem
Existing image processing techniques fail to effectively reduce color noise and false color, especially in high frequency regions, leading to deteriorated image quality due to noise generation from digital imaging apparatuses like CCD or CMOS sensors.
Innovation Solution
An image processing apparatus and method that includes a noise reduction unit, a color difference signal generation unit, and a combining unit to generate hierarchical images and suppress false color by selecting the appropriate color difference signals based on their magnitude, effectively reducing color noise and false color in high frequency regions without blurring edges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If edge-preserving smoothing filter or patch-based noise reduction method is used, then noise reduction accuracy is improved, but coloring (false color) occurs in high frequency regions where few similar patterns exist
Solution Approach 1:
The image is divided into multiple frequency components using wavelet transform, separating high frequency regions (prone to coloring) from low frequency regions. This segmentation allows different processing strategies to be applied to different frequency bands, resolving the contradiction by treating high frequency components differently to avoid coloring while maintaining noise reduction effectiveness.
Solution Approach 2:
Different processing strengths are applied to different regions based on local characteristics. In high frequency regions where similar patterns are scarce, the processing is adjusted to prevent coloring, while in regions with abundant similar patterns, stronger noise reduction is applied. This local adaptation resolves the contradiction by making the processing quality spatially variable.
2Measurement precision
If noise reduction processing is strengthened, then color noise reduction is improved, but image detail and edge sharpness deteriorate
Solution Approach 1:
The noise reduction strength is made dynamic and adaptive rather than uniform. The processing automatically adjusts its intensity based on local image characteristics, applying stronger reduction where noise predominates and weaker reduction where edges and details exist, thus resolving the contradiction between noise reduction and detail preservation.
Solution Approach 2:
Multiple processing parameters are changed and optimized simultaneously, including wavelet decomposition levels, threshold values, and processing strength coefficients. By dynamically adjusting these parameters based on image content, the system achieves effective color noise reduction while preserving image details and edges.
Data Source
AI summary
A noise reduction unit reduces color noise of an input image to generate a noise-reduced image. A first generation unit generates a color difference signal from the noise-reduced image. A reduction processing unit generates hierarchical images including at least two or more reduced images from the noise-reduced image. A second generation unit generates coloring-suppressed color difference signals from the reduced images. A combining unit combines the color difference signal generated by the first generation unit with each of the color difference signals generated by the second generation unit. The second generation unit selects one of the color difference signals of the reduced images for each pixel based on magnitude of each of the color difference signals of the reduced images, to generate the coloring-suppressed color difference signals.