Sparse Structural Elements for Morphological Signal Processing
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Solution Overview
Problem
Existing signal processing methods based on mathematical morphology face challenges in reducing calculation amount and time, especially in noisy environments, and require hardware support for speed improvements.
Innovation Solution
A signal processing method using sparse structural elements and a multi-stage or two-stage sparse algorithm, along with dissociative structural elements and bipolar morphological gradients, to reduce calculation and enhance gradient amplitude.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional morphological filtering is used in noisy environments, then filtering performance is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the structural element into a kernel part and a soft edge part, where the kernel provides strong filtering capability and the soft edge provides noise robustness. This segmentation allows the algorithm to achieve good filtering performance without requiring the full computational complexity of traditional soft morphology, as the kernel can be processed more efficiently.
Solution Approach 2:
The patent introduces sparsity parameter to characterize the structural element, transforming the traditional dense structural element into a sparse one. By changing the density parameter of the structural element, the algorithm achieves both noise robustness and reduced computational complexity, as sparse structures require fewer operations while maintaining filtering effectiveness.
2Productivity
If fast computing methods for morphological operations are used, then processing speed is improved, but algorithm complexity increases or hardware support is required
Solution Approach 1:
The patent extracts and utilizes only the essential computational components of morphological operations by using sparse structural elements. By taking out the core filtering function and removing redundant computations associated with dense structures, the algorithm achieves faster processing speed without requiring complex hardware support or sophisticated fast algorithms.
Solution Approach 2:
The sparse structural element acts as a simplified, computationally inexpensive version of traditional structural elements. It provides the necessary filtering functionality with much lower computational cost, making it a 'cheap' alternative that doesn't require expensive hardware acceleration or complex algorithmic optimizations.
3Reliability
If dense structural elements are used for morphological filtering, then filtering effectiveness is improved, but calculation time increases
Solution Approach 1:
The patent applies local quality by making different parts of the structural element have different densities. The kernel part maintains high density for effective filtering, while the soft edge part uses sparsity for computational efficiency. This local differentiation allows the algorithm to achieve good filtering effectiveness without the uniform computational burden of dense structures throughout.
Solution Approach 2:
The sparse structural element applies partial action by using only the necessary portions of the structural element for effective filtering. Rather than processing all points in a dense structure, the sparse structure processes only critical points, achieving sufficient filtering effectiveness with reduced calculation time.
Data Source
AI summary
A signal processing method based on mathematical morphology with sparse structural elements is disclosed, including the steps of: 1) building sparse structural elements; 2) performing morphological filtering on a signal by using the sparse structural elements; 3) improving a filtering effect for a filtering result by using a multi-stage sparse algorithm or a two-stage sparse algorithm; 4) building dissociative structural elements and a bipolar morphological gradient; and 5) performing a bipolar morphological gradient extraction on the signal by using the dissociative structural elements. The method can effectively reduce the calculation amount and calculation time of mathematical morphology signal processing and enhance the amplitude of the morphological gradient.