A computational approach to color vision enhancement using deep learning, TensorFlow and Keras
Pendhari, Nazneen (University of Mumbai (Índia))
Shaikh, Danish (University of Mumbai (Índia))
Shaikh, Nida (University of Mumbai (Índia))
Nagori, Abdul Gaffar (University of Mumbai (Índia))
Rathod, Kiran (K.J Somaiya Institute of Technology (Índia))

Date:	2025
Abstract:	Individuals afflicted with color vision deficiency (CVD) often face obstacles in effectively navigating and engaging with their surroundings due to challenges in accurately discerning colors. Such limitations can hinder a range of daily activities, compelling these individuals to rely on external assistance for color-centric tasks, potentially curtailing their autonomy and inclusiveness. In response to these impediments, our study centers on the design and implementation of a machine learning-driven color adaptation framework. Utilizing the TensorFlow and Keras libraries, this system harnesses sophisticated machine learning methodologies to detect and modify colors within visual content, thereby augmenting perceptibility for those with CVD. Our principal aim is to equip individuals with CVD with a pragmatic tool that enhances color clarity in images, facilitating self-evaluation of their visual condition. This innovation targets to bolster navigational capabilities, diminish reliance on external assistance for colororiented activities, and advance inclusivity via technological advancements. Furthermore, our investigation underscores the precision and dependability of the machine learning algorithms through meticulous testing and validation protocols, guaranteeing robust performance across diverse contexts and image categories. A user-centric and easily navigable graphical user interface (GUI) is emphasized to accommodate users with varying technical proficiencies. Beyond the immediate technological impact, our research aspires to amplify awareness and deepen comprehension of color vision deficiency within the wider populace, thereby fostering a society characterized by enhanced equity and accessibility for all.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Machine learning ; Image processing ; Keras ; Tensorflow ; Python ; Protanopia ; Deuteranopia ; Tritanopia ; Color vision deficiency ; CVD ; Colorization ; Autoencoder ; Maxpooling ; Upsampling ; Convolutional neural network ; CNN ; Ishihara ; Color blindness ; Adams algorithm ; Tkinter ; MSE ; MAE ; Matplotlib ; Deep learning ; Color transformation
Published in:	ELCVIA. Electronic letters on computer vision and image analysis, Vol. 24 Núm. 1 (2025) , p. 153-179 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/1892
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/980000001037
DOI: 10.5565/rev/elcvia.1892

26 p, 9.6 MB

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