Automated Eye Region Enhancement via Histogram Equalization
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Solution Overview
Problem
Current photograph processing methods require high professional expertise, are time-consuming, and often result in distortion, especially when processing large quantities of images, particularly when it comes to adjusting eyes in portrait photography.
Innovation Solution
A photograph processing method and system that performs face detection, alignment to obtain contour points of the eyes, calculates eye areas, applies stretching transformation, and histogram equalization to enhance the appearance of eyes without manual operation, thereby beautifying them automatically while controlling the processing effect to prevent distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If professional software such as Photoshop is used to process photographs, then the processing quality can be improved, but the operation complexity and time consumption increase significantly
Solution Approach 1:
The system performs automatic face detection, eye region localization, and histogram equalization processing without requiring manual operation. The computer automatically identifies eye areas and applies enhancement algorithms, making the system self-sufficient and eliminating the need for professional manual intervention while maintaining high processing quality
Solution Approach 2:
The system changes the processing parameters by applying histogram equalization specifically to the eye region parameters (brightness, contrast) while keeping other face parameters unchanged. This targeted parameter modification achieves professional-quality enhancement without requiring complex manual adjustment of multiple parameters
2Manufacturing precision
If professional software such as Photoshop is used to process photographs, then the processing quality can be improved, but the time consumption increases significantly
Solution Approach 1:
The system extracts only the eye region from the entire photograph for processing, rather than manually editing the whole image. By isolating and processing only the critical eye areas using automated detection and localization, the system achieves professional-quality enhancement in a fraction of the time required for full-image manual processing
Solution Approach 2:
The system performs preliminary automatic detection and localization of eye regions before applying enhancement algorithms. This pre-processing step automatically identifies and segments the eye areas, preparing them for rapid enhancement processing without requiring time-consuming manual selection and preparation
3Ease of operation
If smart software applications such as Meitu are used to process photographs, then the ease of operation is improved, but the processing effect causes distortion
Solution Approach 1:
The system applies different processing quality levels to different regions: high-quality histogram equalization is applied only to the eye regions while the rest of the face maintains its original quality. This localized approach ensures distortion-free processing of critical areas while maintaining overall image naturalness, achieving both ease of operation and high processing effect quality
4Manufacturing precision
If manual processing methods are used for large quantities of photographs, then the processing quality can be maintained, but the productivity decreases significantly
Solution Approach 1:
The system is designed to universally process multiple photograph formats and sizes through automated face detection and eye region localization. The same algorithmic approach works consistently across large batches of images, maintaining professional processing quality while enabling high-volume throughput that manual methods cannot achieve
Data Source
AI summary
Embodiments of the present invention provide a photograph processing method and system. The method includes: performing face detection on a photograph to obtain a detected human face; performing alignment on the detected human face, so as to obtain contour points of a left eye and a right eye of the detected human face; separately calculating a left eye area, being an area of the left eye, and a right eye area, being an area of the right eye, according to the contour points of the left eye and the right eye; performing stretching transformation on each pixel in the left eye area and the right eye area to generate a stretched left eye area and a stretched right eye area; and performing histogram equalization processing on the stretched left eye area and the stretched right eye area, so as to generate a processed photograph.