Image Sharpness Adjustment via Face Region Segmentation
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Solution Overview
Problem
Existing image processing methods fail to optimally enhance sharpness in images as they do not differentiate between main subjects and background, leading to over-enhancement of non-relevant areas, resulting in suboptimal sharpness adjustment.
Innovation Solution
An image processing method that uses a statistical model, such as Active Appearance Model, to determine a weighting parameter for sharpness adjustment based on the characteristics of a predetermined structure within the image, allowing for targeted sharpness enhancement or reduction specific to the main subject.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If frequency components are enhanced uniformly across the entire image, then sharpness is improved in all areas, but the main subject cannot be differentiated from the background and non-relevant areas are over-enhanced
Solution Approach 1:
The patent applies different sharpness enhancement strengths to different regions of the image based on the detected main subject. The enhancement amount is determined locally for each region rather than uniformly across the entire image, allowing the main subject to be enhanced more while the background receives less enhancement or remains unchanged.
Solution Approach 2:
The image is segmented into a main subject region and a background region based on feature detection and recognition. This segmentation allows the processing system to apply different sharpness enhancement parameters to different segments, specifically targeting the main subject for enhancement while preserving the natural appearance of the background.
2Manufacturing precision
If a statistical model is used to identify the main subject, then targeted sharpness enhancement is achieved, but processing time and computational complexity increase
Solution Approach 1:
The statistical model and feature detection are performed as preliminary steps before the actual sharpness enhancement. By pre-identifying the main subject and determining the appropriate enhancement parameters in advance, the system avoids iterative adjustments during the enhancement process itself, thereby reducing overall processing time while maintaining high accuracy.
Solution Approach 2:
The patent uses parameter changes in the statistical model to efficiently identify the main subject. By adjusting and analyzing specific parameters such as feature weights and statistical characteristics, the system可以快速定位主要对象并确定最佳的锐化参数，在保证准确性的同时减少处理步骤.
Data Source
AI summary
Sharpness is adjusted for more appropriately representing a predetermined structure in an image. A parameter acquisition unit obtains a weighting parameter for a principal component representing a degree of sharpness in a face region detected by a face detection unit as an example of the predetermined structure in the image, by fitting to the face region a mathematical model generated by a statistical method such as AAM based on a plurality of sample images representing human faces in different degrees of sharpness. Based on a value of the parameter, sharpness is adjusted in at least a part of the image. For example, a parameter changing unit changes the value of the parameter to a preset optimal face sharpness value, and an image reconstruction unit reconstructs the image based on the parameter having been changed and outputs the image having been subjected to the sharpness adjustment processing.