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AI

Special Issue：AI-Driven Color Models for Imaging, Formulation, Appearance Measurement and Computer Vision

Special issue link: https://www.mdpi.com/journal/technologies/special_issues/0O2229T6RE

Deadline for manuscript submissions: 30 Aug 2026

Special Issue Editor

Together with Prof.Dr. S. Westland from the University of Leeds, I am special issue editor for journal Technologies.

Artificial Intelligence and Machine Learning have quickly become standard technologies in many fields. Color science and technology is no exception to that, and the number of AI and ML application in this domain is steadily growing. Unfortunately, the publications have so far been scattered over a wide range of journals. Therefore we decided to try bringing together a series of high quality, highly relevant papers that reflect today's status of AI and ML in various subdisciplines of color science and technology.

Technologies is an international, peer-reviewed, quick-refereeing open access journal published online by MDPI, Basel, Switzerland. Technologies (ISSN 2227-7080) is indexed by Scopus, ESCI (Web of Science), Ei Compendex, DOAJ, Inspec, INSPIRE, etc. You can learn more about the scope of the journal here: https://www.mdpi.com/journal/technologies/about/. The Impact Factor of the Journal is 3.6.

You are welcome to contribute an original research paper or a comprehensive review article on the development and/or application of Artificial Intelligence/Machine Learning to color science and technology in the wider sense of the word. This would include applications on imaging, color formulation, color and appearance measurement and computer vision.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Enhanced metrology of reflectance and color based on combined sensor data, such as combining limited sets of LEDs to represent white illuminance, combining RGB image data etc.
  • Improved color formulation of effect coatings and solid colors by combining various types of sensor data.
  • Improved formulation for dyes, textiles, printing and graphical industry
  • Metrology of gloss, haze, visual texture such as sparkle and graininess by combining a variety of sensor data.
  • Detection of scratches by image analysis.
  • Improved color accuracy and/or temporal stability in Augmented Reality applications by AI/ML-based segmentation
  • Enhanced algorithms to convert measurement data between different instruments in the area of reflectance (color) and appearance measurement.
  • Development of Total Appearance Metrics and Total Appearance Difference Metrics by combining data from a variety of instruments.

The emphasis of the articles in this issue will be on gaining insight in color science and technology. Therefore we recommend including such insights in the conclusions section of your article.

We encourage (but obviously do not request) authors to share the code and if possible also the data they used for their investigation on github. This will improve impact of the articles and encourage readers to further engage with the articles. Papers that share their code will be marked as such on the title page.

Dr. Eric Kirchner
Prof. Dr. Steve Westland