An Adaptive Fuzzy-Based System to Simulate, Quantify and Compensate Color Blindness

TL;DR

This paper introduces an adaptive fuzzy logic system for simulating, quantifying, and correcting color blindness, utilizing novel fuzzy-based methods and user surveys to optimize visual distinction improvements.

Contribution

It presents a new fuzzy logic approach for modeling and correcting various degrees of color blindness, including four innovative correction methods.

Findings

Method B with histogram equalization was preferred by 47% of volunteers.

The fuzzy-based correction methods improved color distinction in images.

A web survey validated the effectiveness of the proposed correction techniques.

Abstract

About 8% of the male population of the world are affected by a determined type of color vision disturbance, which varies from the partial to complete reduction of the ability to distinguish certain colors. A considerable amount of color blind people are able to live all life long without knowing they have color vision disabilities and abnormalities. Nowadays the evolution of information technology and computer science, specifically image processing techniques and computer graphics, can be fundamental to aid at the development of adaptive color blindness correction tools. This paper presents a software tool based on Fuzzy Logic to evaluate the type and the degree of color blindness a person suffer from. In order to model several degrees of color blindness, herein this work we modified the classical linear transform-based simulation method by the use of fuzzy parameters. We also proposed four new methods to correct color blindness based on a fuzzy approach: Methods A and B, with and without histogram equalization. All the methods are based on combinations of linear transforms and histogram operations. In order to evaluate the results we implemented a web-based survey to get the best results according to optimize to distinguish different elements in an image. Results obtained from 40 volunteers proved that the Method B with histogram equalization got the best results for about 47% of volunteers.