AI Image Enhancement via Edge Detection and Restricted Histogram Equalization
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Solution Overview
Problem
Variations in image capture quality and environmental conditions lead to non-target features and differing pixel value distributions in images, causing errors in automatic image recognition.
Innovation Solution
An image enhancement processing method using artificial intelligence that includes edge detection, statistics ring processing, migration, and restricted contrast adaptive histogram equalization to isolate and enhance target features, improving image quality and recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used without AI-based enhancement, then the processing flow is simpler, but the recognition accuracy deteriorates due to noise interference and varying image quality
Solution Approach 1:
The image processing is divided into distinct stages: preprocessing to extract feature images, edge detection to identify boundaries, statistics ring generation to define regions of interest, and restricted contrast adaptive histogram equalization to enhance specific areas. This segmentation allows each module to focus on a specific task, improving overall recognition accuracy while maintaining manageable complexity through modular design
Solution Approach 2:
The patent applies different processing strategies to different regions of the image. The statistics ring identifies specific regions where target features are located, and the restricted contrast adaptive histogram equalization enhances contrast only in these identified regions rather than the entire image. This local quality approach improves recognition accuracy in critical areas without unnecessarily complicating the processing of irrelevant regions
2Measurement precision
If comprehensive image processing steps are applied to all images, then the recognition accuracy improves, but the processing time increases
Solution Approach 1:
The preprocessing step performs edge detection and generates the statistics ring before the main enhancement process. By identifying regions of interest in advance through edge detection and statistics ring calculation, the system prepares the necessary information for targeted enhancement, reducing the need for comprehensive processing of the entire image and thereby reducing processing time while maintaining accuracy
Solution Approach 2:
The restricted contrast adaptive histogram equalization applies enhancement only to regions within the statistics ring rather than to the entire image. This partial action approach processes only the necessary portions of the image that contain target features, significantly reducing processing time compared to full-image enhancement while maintaining recognition accuracy in the critical regions
3Measurement precision
If standard histogram equalization is applied to enhance image contrast, then the image quality improves, but noise interference increases due to over-enhancement of non-target areas
Solution Approach 1:
The restricted contrast adaptive histogram equalization modifies the standard algorithm by applying contrast enhancement only within the statistics ring region identified through edge detection and iterative processing. This local quality approach enhances image quality in target feature regions while avoiding over-enhancement of non-target areas, thereby preventing noise generation in irrelevant regions
Solution Approach 2:
The patent extracts the target feature regions from the rest of the image through edge detection and statistics ring generation. By separating the region of interest (within the statistics ring) from the background, the enhanced algorithm applies contrast improvement only to the extracted target areas, preventing noise interference in non-target regions while maintaining high image quality where needed
Data Source
AI summary
An image enhancement processing method includes: acquiring an initial image, preprocessing the initial image, and acquiring an original feature image containing a target feature; performing an edge detection on the original feature image using an edge detection algorithm to obtain an original gradient image, obtaining a statistics ring based on the original feature image, and performing an iterative process on the statistics ring; obtaining a to-be-processed image based on an inner diameter of on the statistics ring, and determining to-be-processed parameters of the to-be-processed image: acquiring a standard image corresponding to the target feature, determining a standard area corresponding to the standard image, and acquiring standard image parameters corresponding to the standard area; performing a migration process on the to-be-processed image to obtain a migration image; and performing a restricted contrast adaptive histogram equalization process on the migration image to obtain a target enhanced image.