Color Conversion for Color Blindness Employing Multilayer Neural Network with Perceptual Model
In this paper, we propose a novel digital image color conversion algorithm for color blindness using a multilayer neural network. The symptoms of “color blindness” are due to an innate lack or deficit of cone cells that recognize colors, and people with color blindness have difficulty discriminating combinations of specific colors. Those people require color conversion for the presented image such that the image can be a perceptible color representation. In the proposed method, we design a multilayer neural network composed of three building blocks: layers for image color conversion, layers for perceptual model of color blindness, and layers for color discrimination. In proposed framework, a neural network is learning about a relationship of an image data and a discrimination performance of colors in an image, and a color conversion rule is trained as a part of a neural network. To validate the effectiveness of proposed method, it is applied to several images that have various color combinations.
KeywordsColor conversion Neural network Color blindness
This work was supported in part by funds (No. 157202) and (No. 155006) from the Central Research Institute of Fukuoka University.
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