A Computational Approach to Color Vision Enhancement Using Deep Learning, Tensorflow and Keras
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.
Keywords
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 TransformationReferences
[1] Hyun-Ji Kim, Jae-Yun Jeong et al., "Color Modification for Color-blind Viewers Using the Dynamic Color Transformation", 2012 IEEE International Conference on Consumer Electronics (ICCE). DOI: https://doi.org/10.1109/ICCE.2012.6162036
[2] Hideaki Orii, Hideaki Kawano et al., "Color Conversion Algorithm for Color Blindness using Self-Organizing Map", SCIS & ISIS 2014, Kitakyushu, Japan, December 3-6, 2014. DOI: https://doi.org/10.1109/SCIS-ISIS.2014.7044811
[3] Jia Bin Huang, Sheng-Jhy Wang et al., "Image Recolorization for the Colorblind", ICASSP 2009. DOI: https://doi.org/10.1109/ICASSP.2009.4959795
[4] George E. Tsekouras, Anastasios Rigos, Stamatis Chatzistamatis et al., "A Novel Approach to Image Recoloring for Color Vision Deficiency", Acoustics, Speech, and Signal Processing, ICASSP-88, 1988 International Conference on, April 2009. DOI: https://doi.org/10.3390/s21082740 https://doi.org/10.3390/s21082740
[5] Giovane R. Kuhn, Manuel M. Oliveira, and Leandro A. F. Fernandes et al., "An Efficient Naturalness-Preserving Image-Recoloring Method for Dichromats", IEEE Transactions on Visualization and Computer Graphics, vol. 14, no. 6, Nov./Dec. 2008. DOI: https://doi.org/10.1109/TVCG.2008.
[6] Bhagya R. Navada, Santhosh K. V. et al., "An Image Processing Technique for Color Detection and Distinguish Patterns with Similar Color: An aid for Color Blind People", Proceedings of International Conference on Circuits, Communication, Control and Computing (I4C 2014). DOI: https://doi. org/10.1109/CIMCA.2014.7057818
[7] Joschua Thomas Simon-Liedtke, Ivar Farup et al., "Evaluating color vision deficiency daltonization meth- ods using a behavioral visual-search method", The Norwegian Colour and Visual Computing Labora- tory, Gjøvik University College, Norway. DOI: https://doi.org/10.1016/j.jvcir.2015. 12.014
[8] Je In You, KeeChan Park et al., "Image Processing with Color Compensation using LCD Display for Color Vision Deficiency", Journal of Display Technology, September 2015. DOI: https://doi.org/10. 1109/JDT.2015.2507189
[9] Jia-Bin Huang, Yu-Cheng Tseng, Se-In Wu et al., "Information Preserving Color Transformation for Protanopia and Deuteranopia", IEEE Signal Processing Letters, vol. 14, no. 10, Oct. 2007. DOI: https: //doi.org/10.1109/LSP.2007.898333 Nazneen Pendhari et al. / Electronic Letters on Computer Vision and Image Analysis 24(1):153-178, 2025 177
[10] Nazneen A. Pendhari et al., "Multimedia Recoloring Technique for Protanopic CVD", Journal of Com- puter Engineering (IOSR-JCE), e-ISSN: 2278-0661, p-ISSN: 2278-8727, PP 57-63, Feb 2018. DOI: https://doi.org/10.3233/IDT-19008
[11] Deepti S. Khurge, Bhagyashree Peshwani et al., "Modifying Image appearance to Improve Information Content for Color Blind viewers", 2015 International Conference on Computing Communication Control and Automation. DOI: https://doi.org/10.1109/ICCUBEA.2015.125https://doi.org/10.1109/ICCUBEA.2015.125
[12] Yihao Liu, Hengyuan Zhao, Kelvin C.K. Chan et al., "Temporally Consistent Video Colorization with Deep Feature Propagation and Self-regularization Learning", Journal of LaTeX Class Files, August 2021. DOI: https://doi.org/10.48550/arXiv.2110.04562
[13] Nazneen A. Pendhari et al., "Video Processing in Visual System for Color detection for people with Tritanomaly", Journal on Information Technology, ISSN: 2277-5110, Vol 7, No 3, PP 17-23, June-August 2018. DOI: https://doi.org/10.26634/jit.7.3.14517https://doi.org/10.26634/jit.7.3.14517
[14] Je In You, KeeChan Park, "Image Processing with Color Compensation using LCD Display", Journal of Display Technology, September 2015.
[15] Jia-Bin Huang, Chu-Song Chen, Tzu-Cheng Jen, Sheng-Jyh Wang, "Image Recolorization for the Colorblind", Academia Sinica, National Chiao Tung University. DOI: https://doi.org/10.1109/ ICASSP.2009.495979
[16] Jia-Bin Huang, Sih-Ying Wu, Chu-Song Chen, "Enhancing Color Representation for the Color Vision Impaired", Workshop on Computer Vision Applications for the Visually Impaired, Oct 2008, Marseille, France.
[17] S. M. W. Masra, A. A. M. A. Shafiee, M. S. Muhammad, "Color Blind Image Correction", Universiti Malaysia Sarawak.
[18] Nazneen A. Pendhari et al., "Image colorization using convolution neural networks", 9th International Conference on Contemporary Engineering and Technology 2021, Chennai, India, April 10-11, 2021.https://doi.org/10.35940/ijitee.E8628.0310521
[19] Giovane R. Kuhn, Manuel M. Oliveira, Leandro A. F. Fernandes, "An Efficient Naturalness-Preserving Image-Recoloring Method for Dichromats", IEEE Transactions on Visualization and Computer Graphics, vol. 14, no. 6, Nov./Dec. 2008. DOI: https://doi.org/10.1109/TVCG.2008.112https://doi.org/10.1109/TVCG.2008.112
[20] Akhan Almagambetov, Senem Velipasalar et al., "Mobile Standards-Based Traffic Light Detection in Assistive Devices for Individuals with Color-Vision Deficiency", IEEE Transactions on Intelligent Trans- portation Systems.
[21] Jia-Bin Huang, Yu-Cheng Tseng, Se-In Wu, Sheng-Jyh Wang, "Information Preserving Color Transfor- mation for Protanopia and Deuteranopia", IEEE.
[22] Nazneen A. Pendhari, Raghavendra R. Sedamkar, Saroj I. Sahdev, Avinash Ingole, "Recoloring of Visual Multimedia using Matlab to aid Color Vision Deficient", Intelligent Decision Technologies, vol. 14, March 2020, pp. 81-100. DOI: https://doi.org/10.3233/IDT-190082https://doi.org/10.3233/IDT-190082
[23] Bhagya R. Navada, Santhosh K. V., Prajwal S., Harikishan B. Shetty, "An Image Processing Technique for Color Detection and Distinguish Patterns with Similar Color: An aid for Color Blind People", Mani- pal Institute of Technology, Manipal, India. DOI: http://dx.doi.org/10.1109/CIMCA.2014. 7057818
[24] Nazneen A. Pendhari et al., "Video and Image recoloring for the color deficient", 4th International Con- ference on Global Technology Initiatives, March 29-30, 2015. 178 Nazneen Pendhari et al. / Electronic Letters on Computer Vision and Image Analysis 24(1):153-178, 2025
[25] Wang S., Xu X., Zhang Y., "A Deep Learning Approach for Colorblind Image Enhancement and Analy- sis", IEEE Transactions on Image Processing, vol. 30, pp. 4572-4586, 2021.
[26] Yuan Z., Zhang S., Liu Z., "Color Correction for Color Blindness Based on Generative Adversarial Net- works", ACM Transactions on Graphics, vol. 39, no. 4, article 95, 2020.
[27] Lee H., Kim J., Choi H., "Improving Color Accessibility in Digital Content with Machine Learning Techniques", Journal of Computer Vision, vol. 137, pp. 62-75, 2019.a
Published
How to Cite
Downloads
Copyright (c) 2025 Dr. Nazneen Pendhari, Danish Razzak Shaikh, Nida Shaikh, Abdul Gaffar Nagori, Dr. Kiran Rathod
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
