Automatic Color Deficient Vision Correction Algorithm
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Solution Overview
Problem
Current solutions for color deficient vision correction require user input and knowledge of necessary corrections, making them inaccessible to individuals who do not know what adjustments to apply, especially for automatic image processing.
Innovation Solution
An automatic method that identifies regions of interest in an image based on color deficiency types, corrects these regions by remapping colors or embedding spatial textures, and produces a modified image without user intervention, using algorithms to detect and correct colors in the LMS color space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automatic color deficient vision correction is implemented, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The system performs self-service by automatically detecting color deficient regions in the image and applying appropriate corrections without requiring user input. The algorithm independently identifies regions of concern based on color deficiency types and applies remapping or spatial texture embedding to correct these regions, making the system self-sufficient and eliminating the need for user expertise.
Solution Approach 2:
The system changes color space parameters by converting images to LMS color space and identifying regions where color values fall within problematic ranges for color deficient individuals. By modifying color parameters in identified regions through remapping or texture embedding, the system automatically corrects the image to improve distinguishability for color deficient viewers.
2Productivity
If automatic correction is applied to the entire image, then productivity is improved, but loss of information increases
Solution Approach 1:
The system applies local quality by correcting only the specific regions of the image that contain colors problematic for color deficient individuals, rather than applying uniform correction to the entire image. By identifying and targeting only the regions of concern for correction, the system preserves the original quality of non-problematic areas while improving accessibility in affected regions.
3Adaptability or versatility
If multiple color deficiency types are detected, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system segments the analysis by evaluating multiple color deficiency types (protanopia, deuteranopia, tritanopia) separately and identifying regions of concern for each type independently. By segmenting the detection process for different deficiency types and then applying appropriate corrections to identified regions, the system achieves comprehensive adaptability while managing complexity through modular analysis.
Data Source
AI summary
A method that includes receiving an image, automatically determining at least one region of interest in the image based on at least one color deficiency type from a plurality of color deficiency types, modifying the image by correcting the at least one region of interest and producing an output of the modified image.