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Abstract

In recent years, with the rapid development of digital printing technology, an increasing number of counterfeit products have entered the market. The anti-counterfeiting technique for QR Codes has been attracting increasing attention nowadays. There exist many image inpainting methods that can be applied in the image restoration field. Some image completion methods may restore coating QR Code images to a point where the covered digital number underneath is revealed even though the original coating QR Code has not been scratched off. In this paper, we extend the pluralistic image completion (PIC) method to scratched coating QR Code image restoration. Based on the binary characteristic of QR Codes, we present a specific type of deep learning model for scratched coating QR Code image completion. Experimental results demonstrate that the extended PIC is an effective approach to the restoration of scratched coating QR Code images.

Digital Library: JIST

FastTrack Article

Impact of Flight Altitude and Photo Control Points on the Accuracy of 2D and 3D Modeling in UAV Surveys: A Case Study of Jurong Dongshan River

25 5

UAV
oblique photography
water conveyance channel
water conservancy facilities

Hongfei Lu, Shan Yang, Hao Zhou, Jin Zhao, Fangfang Bai, Yang Han, Bo Zhen

Pages 1 - 15, © Society for Imaging Science and Technology 2025

DOI

10.2352/J.ImagingSci.Technol.2025.69.4.040506

CAPT 2024 Latest Innovations on Printing and Packaging Technologies

Abstract

Taking the Jurong Dongshan River as an illustrative case, we employed a Dajiang Phantom 4 RTK SE unmanned aerial vehicle (UAV) for single-lens tilt photography. Our investigation focused on examining the influence of varying flight altitudes (FA: 30 m, 60 m and 90 m) and the configuration of photo control points on two-dimentional (2D) or three-dimensional (3D) mapping accuracy of river embankments, slopes, and hydraulic structures, as well as the analysis of outcomes of 2D and 3D modeling under different FA conditions augmented with supplementary photographs. Our observations revealed that the longitude and elevation accuracy at 30 m FA were higher without photo control operations compared to those at 60 m and 90 m FA, and accuracy diminished as FA increased. Specifically, the longitude accuracy of the embankment photo control points exceeded that of the slope photo control points, whereas the elevation accuracy of the embankment photo control points was superior at FAs of 60 m and 90 m. The geographical location deviation of hydraulic structures (irrigation intake gates) in the 2D model was larger than that obtained in the 3D model. Notably, the incorporation of additional detailed photographs significantly augmented the modeling efficacy of UAV aerial survey data, especially in capturing intricate plant and slope details. It is recommended that the Phantom 4 RTK SE be used at FA of 90 m to establish a foundational channel model, along with capture of additional detailed photographs of crucial structures, slopes, etc., and obtaining geographic location information in 3D Models.

Digital Library: JIST

FastTrack Article

23 4

Improvement of Gas Barrier Properties and Antioxidant Activity of PVA/Hemicellulose Active Packaging Films Reinforced by Tea Polyphenols

hemicellulose
tea polyphenols
gas barrier
active packaging

Yonghang Niu, Yining Wang, Haonan Chen, Jian Du, Haisong Wang, Yanna Lv

Pages 1 - 10, © Society for Imaging Science and Technology 2025

DOI

10.2352/J.ImagingSci.Technol.2025.69.3.030407

Abstract

The preparation of active food packaging materials with both high gas barrier and antioxidant properties remains a great challenge. Hemicellulose is an abundant raw material with excellent oxygen barrier performance and biodegradability, and tea polyphenols (TPs) are natural antioxidants with the function of reducing water sensitivity of polysaccharide-based films. Herein, a synergistic strategy is reported for the preparation of active-controlled food packaging material by incorporating TP into the poly(vinyl alcohol) hemicellulose (PVA/HC) matrix. The obtained film with 5 wt% addition of TP exhibited the highest tensile strength of 41.09 MPa, which was increased by 84% than that of the PVA/HC film. Meanwhile, water vapor permeability was decreased by 25% to 7.39 × 10⁻¹² g⋅cm/(cm²⋅s⋅Pa) compared to a PVA/HC film. Moreover, the addition of TP improved the thermal stability of the (PVA/HC/TP)_n film. The outstanding packaging performances were attributed to the robust hydrogen bonding between TP and the PVA/HC matrix as demonstrated by Fourier transform infrared spectroscopy. The DPPH free radical scavenging of (PVA/HC/TP)₁₅ films reached 87.99%, and the film also exhibited enhanced inhibition efficiency against the microorganism E. coli. Compared with the polyethylene plastic wrap, the film showed excellent food packaging applicability by preventing air oxidation of fresh-cut potato. This study sheds light on a design strategy for active packaging films using a natural substance and easily available biomass.

Digital Library: JIST

FastTrack Article

Federated Learning for Image-based Cancer Detection and Diagnosis: A Systematic Review

27 3

cancer
computer-aided detection and diagnosis systems
deep learning
federated learning
privacy-preserving

Ye Jin, Jinshan Tang

Pages 1 - 24, © Society for Imaging Science and Technology 2025

DOI

10.2352/J.ImagingSci.Technol.2025.69.4.040502

CAPT 2024 Latest Innovations on Printing and Packaging Technologies

Abstract

Deep learning (DL) has advanced computer-aided diagnosis, yet the limited data available at local medical centers and privacy concerns associated with centralized AI approaches hinder collaboration. Federated learning (FL) offers a privacy-preserving solution by enabling distributed DL training across multiple medical centers without sharing raw data. This article reviews research conducted from 2016 to 2024 on the use of FL in cancer detection and diagnosis, aiming to provide an overview of the field’s development. Studies show that FL effectively addresses privacy concerns in DL training across centers. Future research should focus on tackling data heterogeneity and domain adaptation to enhance the robustness of FL in clinical settings. Improving the interpretability and privacy of FL is crucial for building trust. This review promotes FL adoption and continued research to advance cancer detection and diagnosis and improve patient outcomes.

Digital Library: JIST

FastTrack Article

27 3

Spectral Color Prediction based on Neugebauer Primary Colors and Improved PSO-DNN Model

printer characterization
spectral prediction model
principal component analysis
Neugebauer
deep neural network

Dongwen Tian, Junwei Qiao, Jinghuan Ge, Na Su

Pages 1 - 9, © Society for Imaging Science and Technology 2025

DOI

10.2352/J.ImagingSci.Technol.2025.69.3.030419

Abstract

Color accuracy in conventional and digital printing processes relies on press characterization to establish the relationship between input device values and output colorimetric or spectral reflectance values. Conventional models, such as Murray–Davies, Clapper–Yule, Yule–Nielsen, and Yule–Nielsen modified spectral Neugebauer, are renowned for providing accurate chromatic and spectral predictions. However, they fall short of accounting for the effects of black ink use and struggle to predict light hues accurately. In order to predict more accurately the color fingerprint, spectral reflectance, of halftone printed images, this study introduces a novel machine-learning-based deep neural network combined with the improved particle swarm optimization algorithm. This enables implementing a spectral reflectance color prediction model for CMYK printing, which eliminates the need to adjust for dot gain during printing. By evaluating this model on a lithographic offset press, we demonstrate its superior performance evidenced by significantly lower root mean square error and color difference (ΔE ∗ 00) values compared to existing methods. This approach minimizes color deviations during printing and reduces material and energy consumption, thereby ultimately enhancing the quality of printed materials.

Digital Library: JIST

FastTrack Article

Spectral Reflectance Reconstruction based on LED Light Sources

25 3

multi-spectral imaging system
spectral reflectance reconstruction
surface spectral reflectance
weighting coefficient

Chun Liang, Long Ma, Enliang Zhao

Pages 1 - 12, © Society for Imaging Science and Technology 2025

DOI

10.2352/J.ImagingSci.Technol.2025.69.2.020509

Work Presented at Electronic Imaging 2025

Abstract

This study proposes an improved spectral reflectance reconstruction method to convert the response data captured by RGB digital cameras to the object surface spectral reflectance. Additionally, a system noise model is also proposed, which incorporates both signal-dependent and signal-independent components, thus rendering it more closely aligned with real-world conditions. Image data captured with RGB digital cameras under multiple LED light sources, and the inverse distance of the Euclidean distance between the training sample test samples in RGB color space is used as a weighting coefficient to reconstruct the spectral reflectance of the object surface, as accurately as possible. Experimental results show that the proposed method has better reconstruction accuracy than existing methods. The root mean square error values and the reconstructed goodness of fit coefficient higher than 0.9958 indicate that the spectral reconstruction performance has greatly improved compared to existing methods, thus proving the validity of the proposed weighting method.

Digital Library: JIST

FastTrack Article

80 11

ExtremeMETA: High-speed Lightweight Image Segmentation Model by Remodeling Multi-channel Metamaterial Imagers

large convolution kernel
model compression
segmentation
meta-material

Quan Liu, Brandon T. Swartz, Ivan Kravchenko, Jason G. Valentine, Yuankai Huo

DOI

10.2352/J.ImagingSci.Technol.2025.69.4.040403

Hybrid Deep Learning Fusion Combined Technology Acceptance Models for Counterfeit Label Classification and Satisfaction Assessment Applications

Abstract

Deep neural networks (DNNs) have heavily relied on traditional computational units, such as CPUs and GPUs. However, this conventional approach brings significant computational burden, latency issues, and high power consumption, limiting their effectiveness. This has sparked the need for lightweight networks such as ExtremeC3Net. Meanwhile, there have been notable advancements in optical computational units, particularly with metamaterials, offering the exciting prospect of energy-efficient neural networks operating at the speed of light. Yet, the digital design of metamaterial neural networks (MNNs) faces precision, noise, and bandwidth challenges, limiting their application to intuitive tasks and low-resolution images. In this study, we proposed a large kernel lightweight segmentation model, ExtremeMETA. Based on ExtremeC3Net, our proposed model, ExtremeMETA maximized the ability of the first convolution layer by exploring a larger convolution kernel and multiple processing paths. With the large kernel convolution model, we extended the optic neural network application boundary to the segmentation task. To further lighten the computation burden of the digital processing part, a set of model compression methods was applied to improve model efficiency in the inference stage. The experimental results on three publicly available datasets demonstrated that the optimized efficient design improved segmentation performance from 92.45 to 95.97 on mIoU while reducing computational FLOPs from 461.07 MMacs to 166.03 MMacs. The large kernel lightweight model ExtremeMETA showcased the hybrid design’s ability on complex tasks.

Digital Library: JIST

FastTrack Article

IMETI 2024 Special Issue

22 2

anti-counterfeit label classification
transfer learning
neighborhood components analysis
Naive Bayes
support vector machine
PLS-SEM

Hua-Ching Chen, Hsuan-Ming Feng

DOI

10.2352/J.ImagingSci.Technol.2025.69.4.040402

Abstract

This article resolutely uses the concept of feature fusion to establish a deep learning model that can quickly recognize objects and complete an anti-counterfeit label recognition system. The receiver combines the training of the technology acceptance model (TAM) to evaluate the satisfaction of users in completing the anti-counterfeit label classification training. In this study, the fusion-based recognition program was employed to extract the feature sets of different categories of anti-counterfeit labels based on the operation of multilayer convolutional neural networks (CNNs) with different depth models. Using neighborhood components analysis, ten important sets of features from different CNN models were selected and reorganized parallelly into a new small-scale feature fusion dataset. By using naive Bayes and support vector machine methods, efficient classification of wine label image feature datasets after fusion was achieved. The feature fusion anti-counterfeiting label recognition system proposed in this article had a maximum recognition accuracy of 99.29% and a data reduction compression ratio of about 1/50. In addition to reducing training time, it maintained a high level of accuracy. This study established a TAM with the advantage of a feature fusion anti-counterfeit label recognition system. The model was tested on 100 consumers, and a satisfaction evaluation and validation analysis with partial least squares structural equation modeling were completed thereafter. The efficiency of the fusion-based deep learning model met the level of consumer satisfaction. This will be beneficial for educating consumers to use and enhance their willingness to promote and repurchase wine products in the future.

Digital Library: JIST

FastTrack Article

Inkjet Defect Detection Method based on Printing Characterization

20 3

inkjet defect detection
onepass printing
optimal matching
printhead alignment error
nozzle malfunction
image processing

Fang Xu, Tao Chen, Hongwu Zhan, Yinwei Zhang

DOI

10.2352/J.ImagingSci.Technol.2025.69.4.040503

CAPT 2024 Latest Innovations on Printing and Packaging Technologies

Abstract

With the advancement of digital printing technology, the rapid detection of inkjet defects have become critical research areas in OnePass inkjet printing. These defects can disrupt the continuity and smoothness of the image, leading to a decrease in print quality. To overcome these issues, this study proposed an inkjet defect detection method based on printing characterization. Image processing technology was used to obtain the printing characterization information of the printer and extract the information of ink point positions. Optimizing the allocation matrix through Sinkhorn algorithm and combining it with Robust Point Matching algorithm to construct the transmission objective function was accomplished to obtain the optimal point set matching model. This model serves two purposes: diagnosing the nozzle function status of each printhead and quantifying the alignment errors between printheads. Experiments demonstrated the high precision of this detection method. We analyzed the impact of related parameters on the model’s performance and assessed changes in image quality under different alignment errors. This approach provides a new solution for optimizing printer maintenance.

Digital Library: JIST

FastTrack Article

30 3

Research on Multi-objective Static Scheduling of Intelligent Printing Workshop based on Learning Effect

intelligent printing workshop
production scheduling
static scheduling
improved NSGA-II algorithm

Linlin Liu, Chenglin Zhao, Shengjie Liu, Ke Lin, Ji Lu, Guangshun Nie, Yaping Zheng

DOI

10.2352/J.ImagingSci.Technol.2025.69.3.030418