Article
A Novel Approach to Image Recoloring for Color
Vision Deficiency
George E. Tsekouras
1,
* , Anastasios Rigos
1
, Stamatis Chatzistamatis
1
, John Tsimikas
2
, Konstantinos Kotis
1
,
George Caridakis
1
and Christos-Nikolaos Anagnostopoulos
1
Citation: Tsekouras, G.E.; Rigos, A.;
Chatzistamatis, S.; Tsimikas, J.; Kotis,
K.; Caridakis, G.; Anagnostopoulos,
C.-N. A Novel Approach to Image
Recoloring for Color Vision
Deficiency. Sensors 2021, 21, 2740.
https://doi.org/10.3390/s21082740
Academic Editor: Fernanda Irrera
Received: 15 March 2021
Accepted: 9 April 2021
Published: 13 April 2021
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4.0/).
1
Department of Cultural Technology and Communications, University of the Aegean, 811 00 Mitilini, Greece;
a.rigos@aegean.gr (A.R.); stami@aegean.gr (S.C.); kotis@aegean.gr (K.K.); gcari@aegean.gr (G.C.);
canag@ct.aegean.gr (C.-N.A.)
2
Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean,
811 00 Mitilini, Greece; tsimikas@aegean.gr
* Correspondence: gtsek@ct.aegean.gr; Tel.: +30-22-510-36631
Abstract:
In this paper, a novel method to modify color images for the protanopia and deuteranopia
color vision deficiencies is proposed. The method admits certain criteria, such as preserving image
naturalness and color contrast enhancement. Four modules are employed in the process. First, fuzzy
clustering-based color segmentation extracts key colors (which are the cluster centers) of the input
image. Second, the key colors are mapped onto the CIE 1931 chromaticity diagram. Then, using the
concept of confusion line (i.e., loci of colors confused by the color-blind), a sophisticated mechanism
translates (i.e., removes) key colors lying on the same confusion line to different confusion lines so
that they can be discriminated by the color-blind. In the third module, the key colors are further
adapted by optimizing a regularized objective function that combines the aforementioned criteria.
Fourth, the recolored image is obtained by color transfer that involves the adapted key colors and the
associated fuzzy clusters. Three related methods are compared with the proposed one, using two
performance indices, and evaluated by several experiments over 195 natural images and six digitized
art paintings. The main outcomes of the comparative analysis are as follows. (a) Quantitative
evaluation based on nonparametric statistical analysis is conducted by comparing the proposed
method to each one of the other three methods for protanopia and deuteranopia, and for each index.
In most of the comparisons, the Bonferroni adjusted p-values are <0.015, favoring the superiority of
the proposed method. (b) Qualitative evaluation verifies the aesthetic appearance of the recolored
images. (c) Subjective evaluation supports the above results.
Keywords:
color vision deficiency; image recoloring; confusion line; chromaticity diagram; fuzzy
clustering; differential evolution; color transfer; natural images; art paintings
1. Introduction
The human trichromatic color vision originates from the comparison of the rates at
which photons are absorbed by three types of photoreceptor cone-cells namely, the L-, M-,
and S-cones [
1
,
2
]. In practice, the above types of cones define the three channels of the LMS
color space, and only as an approximation of their stimulation they correspond to the red,
green, and blue colors, respectively. Color vision deficiency (CVD) (or color-blindness) is
defined as the human eye’s inability to correctly match and perceive colors affecting at least
8% of males and 0.8% of females [
2
,
3
]. It is caused by genetic mutations that lead either to
the absence or dysfunctionality of one or two types of cones [
2
,
4
,
5
]. The impact of CVD
on human vision is reflected on various physiological levels such as color discrimination,
object recognition, color appearance, color naming, etc. [1,6].
There are three categories of CVDs [
1
–
3
,
5
]. The most severe and the rarest one is the
achromatopsia caused by the absence of two types of cones. The second is the dichromacy,
where one type of cones is missing. Dichromacy includes three subcategories: protanopia,
Sensors 2021, 21, 2740. https://doi.org/10.3390/s21082740 https://www.mdpi.com/journal/sensors
