Version 1
: Received: 13 July 2024 / Approved: 18 July 2024 / Online: 18 July 2024 (23:42:27 CEST)
How to cite:
Kwiek, P.; Jakubowska, M. Color Standardization of Chemical Solution Images Using Template-Based Histogram Matching in Deep Learning Regression. Preprints2024, 2024071502. https://doi.org/10.20944/preprints202407.1502.v1
Kwiek, P.; Jakubowska, M. Color Standardization of Chemical Solution Images Using Template-Based Histogram Matching in Deep Learning Regression. Preprints 2024, 2024071502. https://doi.org/10.20944/preprints202407.1502.v1
Kwiek, P.; Jakubowska, M. Color Standardization of Chemical Solution Images Using Template-Based Histogram Matching in Deep Learning Regression. Preprints2024, 2024071502. https://doi.org/10.20944/preprints202407.1502.v1
APA Style
Kwiek, P., & Jakubowska, M. (2024). Color Standardization of Chemical Solution Images Using Template-Based Histogram Matching in Deep Learning Regression. Preprints. https://doi.org/10.20944/preprints202407.1502.v1
Chicago/Turabian Style
Kwiek, P. and Małgorzata Jakubowska. 2024 "Color Standardization of Chemical Solution Images Using Template-Based Histogram Matching in Deep Learning Regression" Preprints. https://doi.org/10.20944/preprints202407.1502.v1
Abstract
Color distortion in an image poses a challenge for classification and regression when the input data consists of pictures. Because of that, a new algorithm for color standardization of photos is proposed, which serves as a base for a deep neural network regression model. This approach utilizes a custom color template, which was developed based on an initial series of studies and digital imaging. Using the equalized histogram of R, G, B channels from the digital template and its photo, a color mapping strategy is computed. By applying this approach, the histograms are adjusted, and the colors of photos taken with a smartphone are standardized. The proposed algorithm has been developed for a series of photos where the entire surface roughly maintains a uniform color, and the differences in color between the photographs of individual objects are minor. The optimized approach was validated in the colorimetric determination procedure of vitamin C. The dataset for deep neural network in the regression variant was formed from photos of samples under two separate lighting conditions. For the concentration range of vitamin C from 0 to 87.72 µg·mL-1, the RMSE for the test set ranged between 0.75 and 1.95 µg·mL-1, in comparison to the non-standardized variant, where this indicator was at the level of 1.48-2.29 µg·mL-1. The consistency of the predicted concentration results with actual data expressed using R2 was between 0.9956 and 0.9999, for each of the standardized variants. This approach allows for the removal of light reflections on the shiny surface of solutions, which is a common problem in liquid samples. The color matching algorithm has a universal character, and its application scope is not limited.
Keywords
color standardization; color template matching; RGB histogram fitting; deep learning; vitamin C colorimetric determination
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
