Adaptive Red-Eye Correction via Patch Classification
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Solution Overview
Problem
Existing red-eye correction methods in digital images often result in image degradation, such as extending beyond the pupil area, poorly defined pupils, or loss of glint, due to inadequate detection and correction techniques.
Innovation Solution
An image processing method that classifies detected red-eye regions into specific classes to apply either standard or modified corrections, reducing the risk of degradation by using a classifier trained to distinguish between different types of degradations and applying tailored correction strategies, including parameterized morphological operations, template-based chromatic pupil correction, and inserting specular reflections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If automated red-eye correction is applied to all detected red regions, then red-eye artifacts are reduced, but image quality degrades due to over-correction and loss of glint
Solution Approach 1:
The patent applies different correction strategies to different regions within the red-eye area. The classifier identifies specific patches as belonging to the pupil region versus the glint region, and applies correction only to the pupil region while preserving the glint region. This localized approach ensures that red-eye is corrected without degrading overall image quality or removing important visual features like glint.
2Productivity
If standard correction is applied to all red-eye patches, then processing is simple and fast, but degradation occurs including extending beyond pupil area and poorly defined pupils
Solution Approach 1:
The patent segments the red-eye region into multiple patches and further classifies each patch as belonging to either the pupil region or the glint region. This segmentation allows the system to apply correction selectively - using standard correction for pupil regions and no correction for glint regions. The segmentation approach maintains processing efficiency while significantly improving correction accuracy by avoiding one-size-fits-all correction.
3Manufacturing precision
If manual verification is required for red-eye correction, then image quality is maintained, but productivity decreases due to operator involvement
Solution Approach 1:
The patent implements an automated classification system that independently identifies and categorizes red-eye patches without requiring manual operator verification. The classifier automatically determines which patches belong to the pupil region and should be corrected, and which patches belong to the glint region and should be preserved. This self-service approach maintains high correction accuracy while eliminating the need for manual review, thereby preserving processing throughput.
4Object-affected harmful factors
If pre-flash hardware is used to prevent red-eye, then red-eye is minimized, but energy consumption increases and shooting delay occurs
Solution Approach 1:
The patent replaces the mechanical/optical pre-flash system with a digital image processing system. Instead of using additional light pulses (pre-flashes) to physically constrict pupils before photographing, the system captures the image with normal flash and then digitally identifies and corrects red-eye artifacts in the captured image. This substitution eliminates the need for additional energy-consuming pre-flash hardware while achieving effective red-eye reduction through automated detection and selective correction algorithms.
Data Source
AI summary
An image processing method takes as its input, a patch of an image where a candidate red-eye has been detected. The method includes classifying the patch with a classifier trained to assign the patch either to a default first class of patches associated with a standard correction of the candidate red-eye or to at least one second class of patches associated with a modified correction of the candidate red-eye. The modified correction may be designed to reduce a risk of degradation of the image for the second class of patches. The classification optionally includes determining a confidence level associated with the classification. If the image is classified into the at least one second class and the confidence level, where determined, exceeds a threshold, the modified correction is applied to the patch, otherwise, the standard correction is applied.