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J. Imaging
Special Issues
Multi-Spectral and Color Imaging: Theory and Application
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Multi-Spectral and Color Imaging: Theory and Application

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Color, Multi-spectral, and Hyperspectral Imaging".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 5444

Special Issue Editors

Prof. Dr. Pierre Gouton

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Guest Editor
Laboratoire ImVia, UFR Sciences et Techniques, Université de Bourgogne, 21078 Dijon, France
Interests: computer vision; robot vision; security access and monitoring; multispectral imaging; medical image processing; agriculture applications
Special Issues, Collections and Topics in MDPI journals
Dr. Hermine Chatoux

E-Mail Website
Guest Editor
Laboratoire ImVia, UFR Sciences et Techniques, Université de Bourgogne, 21078 Dijon, France
Interests: colour and multi spectral imaging; metrology; texture; key-point detection and descritptions
Dr. Mamadou Diarra

E-Mail Website
Guest Editor
Laboratoire Mécanique et Informque, UFR Mathématiques et Informatique, Université Félix Houphouët-Boigny, 22 BP 582 Abidjan 22, Côte d'Ivoire
Interests: computer science; computer vision; artificial intelligence ; multispectral imaging; image processing and applications; information system and software engineering

Special Issue Information

Dear Colleagues,

Color and multispectral imaging research has made spectacular advances in recent years. Multispectral images collect a considerable amount of information about a scene using multiple wavelengths of light, making it easier to distinguish specific scene features. Additionally, color imagery is easily understood by the human eye and can convey a great amount of visual information. Recent contributions and work in multispectral and color imaging have played a crucial role in advances in engineering, computer vision, image analysis, agriculture, geomatics, biology, etc.

In sum, advances in color and multispectral imaging research are essential for the in-depth understanding of complex scenes and phenomena in many scientific and technical fields.

This Special Issue aims to present an overview of recent developments in this field. Contributions can cover topics such as color and light theory, image processing algorithms, pattern and color recognition, camera calibration, multispectral image fusion, image segmentation, and the generation of color images from multispectral data.

Prof. Dr. Pierre Gouton
Dr. Hermine Chatoux
Dr. Mamadou Diarra
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. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • applications of color and multispectral imaging technologies
  • architectures and implementations of color and multipectral imaging methods and systems
  • artificial intelligence
  • big visual data analysis
  • color image processing
  • color imaging
  • color and multispectral image capture, processing, analysis, and reproduction
  • color and multispectral image retrieval and databases
  • color and multispectral information on and through the internet
  • color and spectral approaches to computer vision
  • color image segmentation
  • fusion of color, multispectral, and 3D data
  • high-dimensional data analysis
  • image and video compression
  • image enhancement
  • image restoration
  • image segmentation
  • imaging beyond color and spectra: 3D, BRDF, appearance, fluorescence, etc.
  • learning-based approaches to color and multispectral image analysis
  • multidimensional signal processing approaches to color and multispectral images
  • multispectral data analysis
  • multispectral imaging
  • quality of color and multispectral images
  • real-time processing
  • spectral and color demosaicking
  • spectral image processing
  • spectral imaging

Published Papers (3 papers)

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Research

18 pages, 4430 KiB  
Article
Classification of Cocoa Beans by Analyzing Spectral Measurements Using Machine Learning and Genetic Algorithm
by Kacoutchy Jean Ayikpa, Pierre Gouton, Diarra Mamadou and Abou Bakary Ballo
J. Imaging 2024, 10(1), 19; https://doi.org/10.3390/jimaging10010019 - 08 Jan 2024
Viewed by 1646
Abstract
The quality of cocoa beans is crucial in influencing the taste, aroma, and texture of chocolate and consumer satisfaction. High-quality cocoa beans are valued on the international market, benefiting Ivorian producers. Our study uses advanced techniques to evaluate and classify cocoa beans by [...] Read more.
The quality of cocoa beans is crucial in influencing the taste, aroma, and texture of chocolate and consumer satisfaction. High-quality cocoa beans are valued on the international market, benefiting Ivorian producers. Our study uses advanced techniques to evaluate and classify cocoa beans by analyzing spectral measurements, integrating machine learning algorithms, and optimizing parameters through genetic algorithms. The results highlight the critical importance of parameter optimization for optimal performance. Logistic regression, support vector machines (SVM), and random forest algorithms demonstrate a consistent performance. XGBoost shows improvements in the second generation, followed by a slight decrease in the fifth. On the other hand, the performance of AdaBoost is not satisfactory in generations two and five. The results are presented on three levels: first, using all parameters reveals that logistic regression obtains the best performance with a precision of 83.78%. Then, the results of the parameters selected in the second generation still show the logistic regression with the best precision of 84.71%. Finally, the results of the parameters chosen in the second generation place random forest in the lead with a score of 74.12%. Full article
(This article belongs to the Special Issue Multi-Spectral and Color Imaging: Theory and Application)
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15 pages, 4256 KiB  
Article
Hyperspectral Characteristic Band Selection and Estimation Content of Soil Petroleum Hydrocarbon Based on GARF-PLSR
by Pengfei Shi, Qigang Jiang and Zhilian Li
J. Imaging 2023, 9(4), 87; https://doi.org/10.3390/jimaging9040087 - 20 Apr 2023
Cited by 1 | Viewed by 1058
Abstract
With continuous improvements in oil production, the environmental problems caused by oil exploitation are becoming increasingly serious. Rapid and accurate estimation of soil petroleum hydrocarbon content is of great significance to the investigation and restoration of environments in oil-producing areas. In this study, [...] Read more.
With continuous improvements in oil production, the environmental problems caused by oil exploitation are becoming increasingly serious. Rapid and accurate estimation of soil petroleum hydrocarbon content is of great significance to the investigation and restoration of environments in oil-producing areas. In this study, the content of petroleum hydrocarbon and the hyperspectral data of soil samples collected from an oil-producing area were measured. For the hyperspectral data, spectral transforms, including continuum removal (CR), first- and second-order differential (CR-FD, CR-SD), and Napierian logarithm (CR-LN), were applied to eliminate background noise. At present, there are some shortcomings in the method of feature band selection, such as large quantity, time of calculation, and unclear importance of each feature band obtained. Meanwhile, redundant bands easily exist in the feature set, which seriously affects the accuracy of the inversion algorithm. In order to solve the above problems, a new method (GARF) for hyperspectral characteristic band selection was proposed. It combined the advantage that the grouping search algorithm can effectively reduce the calculation time with the advantage that the point-by-point search algorithm can determine the importance of each band, which provided a clearer direction for further spectroscopic research. The 17 selected bands were used as the input data of partial least squares regression (PLSR) and K-nearest neighbor (KNN) algorithms to estimate soil petroleum hydrocarbon content, and the leave-one-out method was used for cross-validation. The root mean squared error (RMSE) and coefficient of determination (R2) of the estimation result were 3.52 and 0.90, which implemented a high accuracy with only 8.37% of the entire bands. The results showed that compared with the traditional characteristic band selection methods, GARF can effectively reduce the redundant bands and screen out the optimal characteristic bands in the hyperspectral data of soil petroleum hydrocarbon with the method of importance assessment, which retained the physical meaning. It provided a new idea for the research of other substances in soil. Full article
(This article belongs to the Special Issue Multi-Spectral and Color Imaging: Theory and Application)
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16 pages, 16779 KiB  
Article
Spectral Super-Resolution for High Dynamic Range Images
by Yuki Mikamoto, Yoshiki Kaminaka, Toru Higaki, Bisser Raytchev and Kazufumi Kaneda
J. Imaging 2023, 9(4), 83; https://doi.org/10.3390/jimaging9040083 - 14 Apr 2023
Viewed by 2212
Abstract
The images we commonly use are RGB images that contain three pieces of information: red, green, and blue. On the other hand, hyperspectral (HS) images retain wavelength information. HS images are utilized in various fields due to their rich information content, but acquiring [...] Read more.
The images we commonly use are RGB images that contain three pieces of information: red, green, and blue. On the other hand, hyperspectral (HS) images retain wavelength information. HS images are utilized in various fields due to their rich information content, but acquiring them requires specialized and expensive equipment that is not easily accessible to everyone. Recently, Spectral Super-Resolution (SSR), which generates spectral images from RGB images, has been studied. Conventional SSR methods target Low Dynamic Range (LDR) images. However, some practical applications require High Dynamic Range (HDR) images. In this paper, an SSR method for HDR is proposed. As a practical example, we use the HDR-HS images generated by the proposed method as environment maps and perform spectral image-based lighting. The rendering results by our method are more realistic than conventional renderers and LDR SSR methods, and this is the first attempt to utilize SSR for spectral rendering. Full article
(This article belongs to the Special Issue Multi-Spectral and Color Imaging: Theory and Application)
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