Luma Sharpening Logic with Multi-Scale Unsharp Mask Filter
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Solution Overview
Problem
Conventional image processing techniques fail to adequately address image distortions and errors introduced by imaging device components, such as defective pixels and lens imperfections, and are inefficient, often causing loss of image information and inaccuracies in color reproduction.
Innovation Solution
An image signal processing system with a YCC processing pipeline that includes luma sharpening logic, using a multi-scale unsharp mask filter and sharp component determination logic to sharpen the luma component while avoiding noise, and employing sharp lookup tables to core the sharp signals and prevent noise from being sharpened.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional sharpening techniques are applied, then image sharpness is improved, but noise is amplified and edges become distorted
Solution Approach 1:
The patent applies different sharpening strengths to different regions of the image based on local characteristics. High-pass filter coefficients are adaptively adjusted according to local image properties such as edge strength and noise levels, allowing strong sharpening in clean areas while suppressing sharpening in noisy regions. This local adaptation resolves the contradiction by making sharpening strength spatially variable rather than uniform.
Solution Approach 2:
The patent employs dynamic sharpening where filter coefficients are continuously adjusted during processing based on real-time analysis of image data. The system monitors local noise levels and edge characteristics, dynamically modifying the high-pass filter coefficients to optimize sharpening performance while minimizing noise amplification. This dynamic adjustment allows the system to adapt to changing image conditions throughout the processing pipeline.
2Object-affected harmful factors
If multi-scale unsharp mask filter is used, then noise is suppressed, but processing time increases
Solution Approach 1:
The patent segments the sharpening process into multiple scales using a multi-scale unsharp mask filter. Different filter kernels are applied at different scales to handle noise and edges at various levels of detail. By processing noise suppression and edge enhancement separately at multiple resolutions, the system achieves effective noise suppression while managing computational complexity through hierarchical processing.
Solution Approach 2:
The patent performs preliminary processing steps such as converting to YCC color space and applying preliminary filtering before the main sharpening operation. These preliminary actions prepare the image data by reducing computational complexity and pre-processing noise characteristics, allowing the subsequent sharpening operation to be more efficient. The multi-scale filter is applied after these preliminary steps, reducing the overall processing burden.
Data Source
AI summary
Systems, methods, and devices for sharpening image data are provided. One example of an image signal processing system includes a YCC processing pipeline that includes luma sharpening logic. The luma sharpening logic may sharpen the luma component while avoiding sharpening some noise. Specifically, a multi-scale unsharp mask filter may obtain unsharp signals by filtering an input luma component, and sharp component determination logic may determine sharp signals representing differences between the unsharp signals and the luma component. Sharp lookup tables may “core” the sharp signals, which may prevent some noise from being sharpened. Output logic may determine a sharpened output luma signal by combining the sharp signals with, for example, luma component or one of the unsharp signals.