Image Edge Enhancement via Dual Gradient Detection
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Solution Overview
Problem
Conventional image edge enhancement methods, such as the Sobel algorithm, fail to detect tiny edges effectively, resulting in reduced image resolution due to insufficient detail information extraction.
Innovation Solution
A method and device that perform both rough and tiny edge detection by obtaining multiple gradient values and using specific thresholds to determine edge enhancement values, enhancing image resolution by incorporating both types of edge detection results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Sobel algorithm is used for edge enhancement, then processing speed is fast and simplicity is achieved, but image resolution is low due to insufficient detail information extraction
Solution Approach 1:
The patent segments the edge detection process into two distinct stages: rough edge detection using Sobel algorithm and tiny edge detection using a separate detection mechanism. This segmentation allows each stage to specialize in detecting different edge types, with the rough edge detection providing fast processing and the tiny edge detection capturing detailed information, thereby resolving the contradiction between processing speed and image resolution.
Solution Approach 2:
The patent applies different edge detection strategies to different regions of the image based on their characteristics. Rough edges are detected using the Sobel algorithm for fast processing, while tiny edges are detected using a specialized detection mechanism for detailed information extraction. This local differentiation allows the system to optimize both processing speed and resolution by applying the appropriate method to each region.
2Device complexity
If only rough edge detection is performed, then processing is simple and fast, but tiny edges are missed resulting in lost detail information
Solution Approach 1:
The patent divides the edge detection function into two segments: rough edge detection for capturing major edges and tiny edge detection for capturing detailed information. This segmentation ensures that both types of edges are detected using appropriate methods, preventing information loss while maintaining reasonable process complexity through modular design.
Solution Approach 2:
The patent performs partial edge detection by focusing on tiny edges that are often overlooked in conventional methods. By adding this partial detection stage specifically targeting tiny edges, the system recovers lost detail information without requiring complete redesign of the entire detection process, thus balancing complexity and information recovery.
3Measurement precision
If multiple gradient values are obtained simultaneously for both rough and tiny edge detection, then image resolution is improved, but processing time increases
Solution Approach 1:
The patent segments the processing into sequential stages: first performing rough edge detection with Sobel algorithm, then performing tiny edge detection. This segmentation allows the system to process different types of edges in sequence rather than simultaneously, improving resolution through comprehensive detection while managing processing time through staged execution.
Solution Approach 2:
The patent performs rough edge detection as a preliminary action before tiny edge detection. By first identifying and processing rough edges, the system establishes a baseline that can guide subsequent tiny edge detection, potentially optimizing the overall processing time by using the preliminary results to inform the detailed detection process.
Data Source
AI summary
A method and a device for enhancing an edge of an image are provided. The method includes: obtaining a first gradient value of a pixel; determining whether the pixel is at a rough edge according to the first gradient value; if yes, obtaining a first edge enhancement value of the pixel and obtaining a first edge enhancement result of the pixel according to the first edge enhancement value; if no, obtaining a second gradient value of the pixel; determining whether the pixel is at a tiny edge according to the second gradient value; if yes, obtaining a second edge enhancement value of the pixel and obtaining a second edge enhancement result of the pixel according to the second edge enhancement value; if no, obtaining the pixel value of the pixel as the edge enhancement result of the pixel; and repeating above steps until each pixel of the image is processed.