Directional Edge Detection via Gradient Statistics
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Solution Overview
Problem
Current image edge detection methods struggle with accurate detection of directional edges, especially in cases of weak edges or strong textures, leading to inaccurate edge information for image sharpening and noise reduction.
Innovation Solution
The proposed method performs a convolution operation on the image using gradient operators in perpendicular directions to obtain gradient data, followed by gradient statistical calculations within a neighboring area to determine edge significance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional edge detection method uses gradient calculation with Sobel operator to detect directional edges, then the detection process is simple and fast, but the detection accuracy deteriorates for weak edges or strong textures leading to misjudgment
Solution Approach 1:
The patent segments the gradient calculation process into multiple directional components (horizontal gradient Gx and vertical gradient Gy) calculated separately using Sobel operators. This segmentation allows independent analysis of gradient magnitudes in different directions, improving the ability to distinguish true edges from textures while maintaining computational efficiency through modular operation.
Solution Approach 2:
The patent introduces a new dimension of analysis by computing edge significance not just from single-pixel gradients but from statistical properties (mean and standard deviation) of gradients across neighboring pixel regions. This dimensional expansion from point-based to region-based analysis enhances detection accuracy for weak edges and textured areas without proportionally increasing complexity.
2Measurement precision
If gradient statistical calculation is performed within neighboring area to improve edge detection accuracy, then the detection precision is improved, but the computational complexity increases
Solution Approach 1:
The patent applies partial action by performing gradient statistical calculations only within limited neighboring areas (local windows) rather than across the entire image. This localized approach computes mean and standard deviation of gradients in small regions around each pixel, providing improved accuracy through statistical analysis while avoiding the excessive computational burden of global statistics.
Solution Approach 2:
The patent changes the parameter representation from raw gradient values to statistical parameters (mean gradient magnitude and standard deviation) that characterize the distribution of gradients in neighboring regions. This parameter transformation enables more robust edge significance determination that is less sensitive to noise and texture variations, improving precision without requiring exponentially more computational resources.
Data Source
AI summary
An image edge detection method for processing an image including multiple pixels includes performing a convolution operation on the image by a gradient operator in a first direction and a gradient operator in a second direction to obtain a first-direction gradient data and a second-direction gradient data, wherein the first direction is perpendicular to the second direction, performing a gradient statistical calculation within a neighboring area of a target pixel of the image according to the first-direction gradient data and the second-direction gradient data to obtain a gradient statistic, and determining an edge significance corresponding to the target pixel according to the gradient statistic.