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Applied Sciences
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Advanced Spectral Imaging Applications: Characterization, Detection and Classification
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Advanced Spectral Imaging Applications: Characterization, Detection and Classification

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 5011

Special Issue Editor

Dr. Ningfang Liao

E-Mail Website
Guest Editor
State Key Discipline Laboratory of Color Science and Engineering and State Education Ministry Key Laboratory of Photoelectronic Imaging Technology and Systems, School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China
Interests: hyper-spectral and multi-spectral imaging; color science; color imaging technology

Special Issue Information

Dear Colleagues,

Spectral imaging technology is a combination of imaging and spectral detection technology, which can obtain spectral information on any pixel of the target. According to the spectral resolution, spectral imaging technologies include multi-spectral imaging and hyper-spectral imaging. According to the spectral range, they include ultraviolet, visible, and infrared spectral imaging. According to the optical system, they include scanning, snapshot, and compressed sensing. Advanced spectral imaging technologies are fundamental tools for the development of many fields. They are extensively applied in fields such as biology, geography, agriculture, medical treatment, military, printing industry, aerospace, etc. Specifically, spectral information can be utilized for geological detection, material classification, component analysis, remote sensing imaging, color reproduction, and so on. Therefore, this Special Issue aims to present advanced spectral imaging technologies and applications, including new methods and experimental results from theoretical research to application.

This Special Issue focuses on, but is not limited to, ultraviolet, visible, and infrared spectral imaging technologies, multi-spectral and hyper-spectral imaging, applications in characterization, detection, classification, remote sensing, and color reproduction, as well as other issues in spectral imaging and applications.

Dr. Ningfang Liao
Guest Editor

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Keywords

  • spectral imaging
  • spectral imaging applications
  • multi-spectral imaging
  • hyper-spectral imaging

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Published Papers (4 papers)

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Research

21 pages, 6648 KiB  
Article
UR-Net: An Optimized U-Net for Color Painting Segmentation
by Zhen Liu, Shuo Fan, Silu Liu and Li Liu
Appl. Sci. 2024, 14(21), 10005; https://doi.org/10.3390/app142110005 - 1 Nov 2024
Viewed by 905
Abstract
The pigments of cultural color paintings have faded with the passage of time. Color segmentations are essential for digital color reconstruction, but the complexity of color paintings makes it challenging to achieve high-precision segmentation using previous methods. To address the challenges of color [...] Read more.
The pigments of cultural color paintings have faded with the passage of time. Color segmentations are essential for digital color reconstruction, but the complexity of color paintings makes it challenging to achieve high-precision segmentation using previous methods. To address the challenges of color painting segmentation, an optimized strategy based on U-Net is proposed in this paper. The residual blocks of a residual network (ResNet) are added to the original U-Net architecture, and a UR-Net is constructed for the semantic segmentation of color paintings. The following steps are taken. First, datasets of color paintings are obtained as training and test samples and are labeled with the two following pixel colors: earth red and ultramarine blue. Second, residual blocks are improved and added to fit the U-Net architecture. Then, a UR-Net is constructed and trained using the samples to obtain the semantic segmentation model. Finally, the effectiveness of the trained UR-Net model for segmenting the test samples is evaluated, and it is compared with the K-means clustering algorithm, ResNet, and U-Net. Data from several studies suggest that the segmentation accuracy of the UR-Net model is higher than that of other methods for the color segmentation of painted images, and the IoUs of the segmented earth red and ultramarine blue pixels are 0.9346 and 0.9259, respectively, achieving the desired results. The proposed UR-Net model provides theoretical and methodological support for further in-depth research on color recognition and segmentation of cultural color paintings. Full article
(This article belongs to the Special Issue Advanced Spectral Imaging Applications: Characterization, Detection and Classification)
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24 pages, 23168 KiB  
Article
Prediction of Fading for Painted Cultural Relics Using the Optimized Gray Wolf Optimization-Long Short-Term Memory Model
by Zhen Liu, An-Ran Zhao and Si-Lu Liu
Appl. Sci. 2024, 14(21), 9735; https://doi.org/10.3390/app14219735 - 24 Oct 2024
Cited by 2 | Viewed by 957
Abstract
Cultural heritage digitization is of great significance for the protection, restoration, and rejuvenation of cultural relics. In particular, the investigation of fading mechanisms is essential for virtual restoration to accurately recreate the original appearance of artifacts and facilitate humanistic and historical research. For [...] Read more.
Cultural heritage digitization is of great significance for the protection, restoration, and rejuvenation of cultural relics. In particular, the investigation of fading mechanisms is essential for virtual restoration to accurately recreate the original appearance of artifacts and facilitate humanistic and historical research. For the purpose of investigating the color fading mechanism of pigments, we propose a color fading time series model using a combined long short-term memory recurrent neural network modified by the gray wolf optimization algorithm (GWOAD-LSTM). First, the general gray wolf algorithm was scaled up to two dimensions and combined with an LSTM model for optimal parameter search. Second, six pigments commonly used in painted artifacts were subjected to simulated aging experiments. Third, by applying the experimental data to different predictors, the results of the Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM), Long Short-Term Memory on Gray Wolf Optimizer (GWO-LSTM), and GWOAD-LSTM models were compared. The results showed that our proposed GWOAD-LSTM model outperformed other models in terms of accuracy and generalization ability, especially in predicting hLC color attributes. Our study aimed to provide a new application tool for the restoration and rejuvenation of painted artifacts. Full article
(This article belongs to the Special Issue Advanced Spectral Imaging Applications: Characterization, Detection and Classification)
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14 pages, 8318 KiB  
Article
A Study on the Color Prediction of Ancient Chinese Architecture Paintings Based on a Digital Color Camera and the Color Design System
by Guang Lv, Ningfang Liao, Chang Yuan, Lizhong Wei and Yunpeng Feng
Appl. Sci. 2024, 14(13), 5916; https://doi.org/10.3390/app14135916 - 6 Jul 2024
Cited by 3 | Viewed by 1188
Abstract
Color paintings such as painted facades and interiors are important decoration elements of ancient Chinese architectures. The color of the paintings usually fades over time due to exposure to strong light, high humidity, high temperatures, and other environmental factors. In order to restore [...] Read more.
Color paintings such as painted facades and interiors are important decoration elements of ancient Chinese architectures. The color of the paintings usually fades over time due to exposure to strong light, high humidity, high temperatures, and other environmental factors. In order to restore or reproduce the color appearance of ancient architecture paintings correctly, it was necessary to study the color degradation process of such paintings. To meet the needs of on-site colorimetric measurement of the paintings on ancient Chinese architectures, we propose using a digital color camera and the CDS (Color Design System) to measure and evaluate the colors of such paintings. The CDS is a color order system recommended by the Chinese national technical committee for color standardization (SAC/TC 120) in 2017 (GB/Z 35473-2017). The current version of the CDS atlas contains about 2740 samples which were uniformly distributed on the whole color space, and can be used to set up the colorimetric characterization model for the digital camera. Particularly, the digital CDS lookup table contains over 400 thousand samples, and it can be used to express the color of paintings on ancient Chinese architectures. In the experiment, a digital color camera was used to capture the colors of the paintings on the ancient Chinese architectures of different years based on the CDS and polynomial transform method. Moreover, a linear interpolation method was proposed for calculating and predicting the color degradation of such paintings. The experimental results show that with the increase in years, the color hue of the paintings changes slowly, while the lightness and the chroma of them fade obviously. In the future, more ancient architectures of different years and from different places should be selected as experimental samples to improve the method and the results of the paper. Full article
(This article belongs to the Special Issue Advanced Spectral Imaging Applications: Characterization, Detection and Classification)
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13 pages, 16687 KiB  
Article
A Novel Line-Scan Algorithm for Unsynchronised Dynamic Measurements
by Simon Verspeek, Thomas De Kerf, Bart Ribbens, Xavier Maldague, Steve Vanlanduit and Gunther Steenackers
Appl. Sci. 2024, 14(1), 235; https://doi.org/10.3390/app14010235 - 27 Dec 2023
Viewed by 1284
Abstract
In non-destructive inspections today, the size of the sample being examined is often limited to fit within the field of view of the camera being used. When examining larger specimens, multiple image sequences need to be stitched together into one image. Due to [...] Read more.
In non-destructive inspections today, the size of the sample being examined is often limited to fit within the field of view of the camera being used. When examining larger specimens, multiple image sequences need to be stitched together into one image. Due to uneven illumination, the combined image may have artificial defects. This manuscript provides a solution for performing line-scan measurements from a sample and combining the images to avoid these artificial defects. The proposed algorithm calculates the pixel shift, either through checkerboard detection or by field of view (FOV) calculation, for each image to create the stitched image. This working principle eliminates the need for synchronisation between the motion speed of the object and the frame rate of the camera. The algorithm is tested with several cameras that operate in different wavelengths (ultraviolet (UV), visible near infrared (Vis-NIR) and long-wave infrared (LWIR)), each with the corresponding light sources. Results show that the algorithm is able to achieve subpixel stitching accuracy. The side effects of heterogeneous illumination can be solved using the proposed method. Full article
(This article belongs to the Special Issue Advanced Spectral Imaging Applications: Characterization, Detection and Classification)
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