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Recent Trends and Advances in Color and Spectral Sensors

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (1 May 2024) | Viewed by 15394

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Special Issue Editors

Dr. Qiang Liu

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Guest Editor

Research center of graphic communication, printing and packaging, Wuhan University, Wuhan 430079, China
Interests: spectral printing; ICC color management; printing quality controlling; fine art reproduction; chromatic adaptation; LED lighting design
Special Issues, Collections and Topics in MDPI journals

Dr. Jean-Baptiste Thomas

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Guest Editor

Department of Computer Science, Norwegian University of Science and Technology, 2806 Gjøvik, Norway
Interests: spectral imaging; material appearance; colour imaging; computational appearance
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Dr. Xufen Xie

E-Mail Website
Guest Editor

Research Institute of Photonics, Dalian Polytechnic University, Dalian 116038, China
Interests: color imaging
Special Issues, Collections and Topics in MDPI journals

Dr. Guangyuan Wu

E-Mail Website
Guest Editor

Faculty of Light Industry, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
Interests: spectral imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Color and spectral sensors are the key technology for color acquisition and reproduction in different applications, such as image visualization, cultural heritage, color recognition and inspection, medical diagnosis, remote sensing, and so on. Color Acquisition and reproduction issues are not comprehensively investigated and are challenging problems in developing a general color accuracy sensing system. Furthermore, test benchmark design, experimental guidelines, visual perception and numerical analysis models for evaluating the performance of color and spectral sensors is also key to implementing accurate material-aware color. At the same time, new areas in the study of applied color and spectral sensors have emerged in just the past decade, examining the new advances in cameras, such as displays, smart-phone, VR, AR, MR and smart lighting.

Manuscripts should contain both theoretical and practical/experimental results. Potential topics include but are not limited to the following: Color sensors, spectral sensors, image sensor, hyper/multispectral imaging, imaging spectroscopy, color/spectral filter array, radiometric calibration, calibration site design, CCD/CMOS, spectral recovery, color perception, color appearance model and image vision.

Dr. Qiang Liu
Dr. Jean-Baptiste Thomas
Dr. Xufen Xie
Dr. Guangyuan Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

color sensors
spectral sensors
imaging spectroscopy
spectral recovery
color perception
color appearance model
color reproduction
image vision

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Published Papers (6 papers)

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Research

16 pages, 16186 KiB

Open AccessCommunication

Color Conversion of Wide-Color-Gamut Cameras Using Optimal Training Groups

by Yasheng Li, Ningfang Liao, Yumei Li, Hongsong Li and Wenmin Wu

Sensors 2023, 23(16), 7186; https://doi.org/10.3390/s23167186 - 15 Aug 2023

Cited by 1 | Viewed by 1517

Abstract

The colorimetric conversion of wide-color-gamut cameras plays an important role in the field of wide-color-gamut displays. However, it is rather difficult for us to establish the conversion models with desired approximation accuracy in the case of wide color gamut. In this paper, we propose using an optimal method to establish the color conversion models that change the RGB space of cameras to the XYZ space of a CIEXYZ system. The method makes use of the Pearson correlation coefficient to evaluate the linear correlation between the RGB values and the XYZ values in a training group so that a training group with optimal linear correlation can be obtained. By using the training group with optimal linear correlation, the color conversion models can be established, and the desired color conversion accuracy can be obtained in the whole color space. In the experiments, the wide-color-gamut sample groups were designed and then divided into different groups according to their hue angles and chromas in the CIE1976L*a*b* space, with the Pearson correlation coefficient being used to evaluate the linearity between RGB and XYZ space. Particularly, two kinds of color conversion models employing polynomial formulas with different terms and a BP artificial neural network (BP-ANN) were trained and tested with the same sample groups. The experimental results show that the color conversion errors (CIE1976L*a*b* color difference) of the polynomial transforms with the training groups divided by hue angles can be decreased efficiently. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Color and Spectral Sensors)

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18 pages, 9423 KiB

Open AccessArticle

Colorimetric Characterization of Color Imaging System Based on Kernel Partial Least Squares

by Siyu Zhao, Lu Liu, Zibing Feng, Ningfang Liao, Qiang Liu and Xufen Xie

Sensors 2023, 23(12), 5706; https://doi.org/10.3390/s23125706 - 19 Jun 2023

Cited by 3 | Viewed by 1923

Abstract

Colorimetric characterization is the basis of color information management in color imaging systems. In this paper, we propose a colorimetric characterization method based on kernel partial least squares (KPLS) for color imaging systems. This method takes the kernel function expansion of the three-channel response values (RGB) in the device-dependent space of the imaging system as input feature vectors, and CIE-1931 XYZ as output vectors. We first establish a KPLS color-characterization model for color imaging systems. Then we determine the hyperparameters based on nested cross validation and grid search; a color space transformation model is realized. The proposed model is validated with experiments. The CIELAB, CIELUV and CIEDE2000 color differences are used as evaluation metrics. The results of the nested cross validation test for the ColorChecker SG chart show that the proposed model is superior to the weighted nonlinear regression model and the neural network model. The method proposed in this paper has good prediction accuracy. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Color and Spectral Sensors)

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23 pages, 10557 KiB

Open AccessArticle

Spectral Filter Selection Based on Human Color Vision for Spectral Reflectance Recovery

by Shijun Niu, Guangyuan Wu and Xiaozhou Li

Sensors 2023, 23(11), 5225; https://doi.org/10.3390/s23115225 - 31 May 2023

Cited by 2 | Viewed by 1447

Abstract

Spectral filters are an important part of a multispectral acquisition system, and the selection of suitable filters can improve the spectral recovery accuracy. In this paper, we propose an efficient human color vision-based method to recover spectral reflectance by the optimal filter selection. The original sensitivity curves of the filters are weighted using the LMS cone response function. The area enclosed by the weighted filter spectral sensitivity curves and the coordinate axis is calculated. The area is subtracted before weighting, and the three filters with the smallest reduction in the weighted area are used as the initial filters. The initial filters selected in this way are closest to the sensitivity function of the human visual system. After the three initial filters are combined with the remaining filters one by one, the filter sets are substituted into the spectral recovery model. The best filter sets under L-weighting, M-weighting, and S-weighting are selected according to the custom error score ranking. Finally, the optimal filter set is selected from the three optimal filter sets according to the custom error score ranking. The experimental results demonstrate that the proposed method outperforms existing methods in spectral and colorimetric accuracy, which also has good stability and robustness. This work will be useful for optimizing the spectral sensitivity of a multispectral acquisition system. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Color and Spectral Sensors)

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21 pages, 6626 KiB

Open AccessArticle

Determination and Measurement of Melanopic Equivalent Daylight (D65) Illuminance (mEDI) in the Context of Smart and Integrative Lighting

by Vinh Quang Trinh, Peter Bodrogi and Tran Quoc Khanh

Sensors 2023, 23(11), 5000; https://doi.org/10.3390/s23115000 - 23 May 2023

Cited by 5 | Viewed by 4406

Abstract

In the context of intelligent and integrative lighting, in addition to the need for color quality and brightness, the non-visual effect is essential. This refers to the retinal ganglion cells (

i p R G C s

) and their function, which were [...] Read more.

In the context of intelligent and integrative lighting, in addition to the need for color quality and brightness, the non-visual effect is essential. This refers to the retinal ganglion cells (

i p R G C s

) and their function, which were first proposed in 1927. The melanopsin action spectrum has been published in CIE S 026/E: 2018 with the corresponding melanopic equivalent daylight (D65) illuminance (

m E D I

), melanopic daylight (D65) efficacy ratio (

m D E R

), and four other parameters. Due to the importance of

m E D I

and

m D E R

, this work synthesizes a simple computational model of

m D E R

as the main research objective, based on a database of 4214 practical spectral power distributions (SPDs) of daylight, conventional, LED, and mixed light sources. In addition to the high correlation coefficient

R^{2}

of 0.96795 and the 97% confidence offset of 0.0067802, the feasibility of the

m D E R

model in intelligent and integrated lighting applications has been extensively tested and validated. The uncertainty between the

m E D I

calculated directly from the spectra and that obtained by processing the RGB sensor and applying the

m D E R

model reached ± 3.3% after matrix transformation and illuminance processing combined with the successful

m D E R

calculation model. This result opens the potential for low-cost RGB sensors for applications in intelligent and integrative lighting systems to optimize and compensate for the non-visual effective parameter

m E D I

using daylight and artificial light in indoor spaces. The goal of the research on

R G B

sensors and the corresponding processing method are also presented and their feasibility is methodically demonstrated. A comprehensive investigation with a huge amount of color sensor sensitivities is necessary in a future work of other research. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Color and Spectral Sensors)

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23 pages, 20552 KiB

Open AccessArticle

ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis

by Laura Nicolás-Sáenz, Agapito Ledezma, Javier Pascau and Arrate Muñoz-Barrutia

Sensors 2023, 23(6), 3338; https://doi.org/10.3390/s23063338 - 22 Mar 2023

Cited by 5 | Viewed by 3016

Abstract

Classifying pixels according to color, and segmenting the respective areas, are necessary steps in any computer vision task that involves color images. The gap between human color perception, linguistic color terminology, and digital representation are the main challenges for developing methods that properly classify pixels based on color. To address these challenges, we propose a novel method combining geometric analysis, color theory, fuzzy color theory, and multi-label systems for the automatic classification of pixels into 12 conventional color categories, and the subsequent accurate description of each of the detected colors. This method presents a robust, unsupervised, and unbiased strategy for color naming, based on statistics and color theory. The proposed model, “ABANICCO” (AB ANgular Illustrative Classification of COlor), was evaluated through different experiments: its color detection, classification, and naming performance were assessed against the standardized ISCC–NBS color system; its usefulness for image segmentation was tested against state-of-the-art methods. This empirical evaluation provided evidence of ABANICCO’s accuracy in color analysis, showing how our proposed model offers a standardized, reliable, and understandable alternative for color naming that is recognizable by both humans and machines. Hence, ABANICCO can serve as a foundation for successfully addressing a myriad of challenges in various areas of computer vision, such as region characterization, histopathology analysis, fire detection, product quality prediction, object description, and hyperspectral imaging. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Color and Spectral Sensors)

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Graphical abstract

21 pages, 8270 KiB

Open AccessArticle

Optimized Method Based on Subspace Merging for Spectral Reflectance Recovery

by Yifan Xiong, Guangyuan Wu and Xiaozhou Li

Sensors 2023, 23(6), 3056; https://doi.org/10.3390/s23063056 - 12 Mar 2023

Cited by 3 | Viewed by 1447

Abstract

The similarity between samples is an important factor for spectral reflectance recovery. The current way of selecting samples after dividing dataset does not take subspace merging into account. An optimized method based on subspace merging for spectral recovery is proposed from single RGB trichromatic values in this paper. Each training sample is equivalent to a separate subspace, and the subspaces are merged according to the Euclidean distance. The merged center point for each subspace is obtained through many iterations, and subspace tracking is used to determine the subspace where each testing sample is located for spectral recovery. After obtaining the center points, these center points are not the actual points in the training samples. The nearest distance principle is used to replace the center points with the point in the training samples, which is the process of representative sample selection. Finally, these representative samples are used for spectral recovery. The effectiveness of the proposed method is tested by comparing it with the existing methods under different illuminants and cameras. Through the experiments, the results show that the proposed method not only shows good results in terms of spectral and colorimetric accuracy, but also in the selection representative samples. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Color and Spectral Sensors)

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