Dominant Gradient Direction Estimation via Arctangent Ratio
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Solution Overview
Problem
Conventional edge detection techniques for image noise reduction are time-consuming and computationally expensive due to the need to determine the dominant gradient direction (DGD) for each image area, which is particularly problematic for high-resolution images.
Innovation Solution
The method determines the dominant gradient direction of an image area by sampling at four locations where one direction is perpendicular to another, calculating gradient magnitudes, and using the arctangent of their ratio, significantly reducing the computational resources and time required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional edge detection techniques determine the dominant gradient direction for each image area, then edge detection accuracy is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent divides the image into discrete blocks (e.g., 8x8 pixel blocks) and processes each block independently to determine its dominant gradient direction. This segmentation allows the system to focus computational resources on local edge characteristics rather than processing the entire image uniformly, reducing overall processing time while maintaining edge detection accuracy at the block level.
Solution Approach 2:
Instead of computing gradient directions for all possible orientations in each image area, the patent calculates only the dominant gradient direction based on the gradient magnitude and angle at each pixel location. This partial action approach computes only the necessary information (the strongest gradient direction) rather than exhaustively analyzing all potential edge orientations, significantly reducing computational complexity.
2Reliability
If conventional methods calculate dominant gradient direction for all image areas, then comprehensive edge detection is achieved, but computational expense increases
Solution Approach 1:
The patent extracts only the essential information needed for edge detection: the gradient magnitude and angle at each pixel, followed by determining the dominant gradient direction for each block. By extracting only these critical parameters and discarding redundant computational steps (such as evaluating all possible gradient directions), the system achieves comprehensive edge detection with reduced computational expense.
Solution Approach 2:
The patent transforms the gradient vector components (gx, gy) into polar coordinates (magnitude and angle), then uses these transformed parameters to determine the dominant gradient direction. This parameter change from Cartesian to polar representation simplifies the identification of the dominant direction by directly utilizing the angle parameter, reducing the computational burden compared to analyzing gradient components in the original coordinate system.
3Measurement precision
If detailed gradient analysis is performed on high-resolution images, then edge detection precision is improved, but processing complexity increases
Solution Approach 1:
The patent processes high-resolution images by dividing them into smaller blocks and determining the dominant gradient direction for each block independently. This segmentation reduces processing complexity by breaking down the large-scale problem into manageable local problems, while still maintaining high overall precision because each block is analyzed in detail for its local edge characteristics.
Solution Approach 2:
For each pixel within a block, the patent calculates the gradient magnitude and angle, then determines the dominant gradient direction for the entire block based on these partial calculations. Rather than performing exhaustive gradient analysis for every possible direction and scale, the method focuses on computing the gradient at each pixel and aggregating this information to find the dominant direction, reducing processing complexity while maintaining precision.
Data Source
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AI summary
The solution disclosed herein reduces the amount of time and computational resources necessary to determine a dominant gradient direction of an image area comprising a plurality of pixels of an image. To that end, the dominant gradient direction of an image area is determined based on two gradient magnitudes determined from four sample points in the image area, where a direction of one of the gradient magnitudes is perpendicular to a direction of the other of the gradient magnitudes. The dominant gradient direction is then determined by taking the arctangent of the computed gradient magnitudes.