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Advancements in Digital Holography for Crystalline Material Characterization: A Review (Invited)

CRYSTAL RESEARCH AND TECHNOLOGY 2025年第4期60卷

作者： Kang, Wenrui Zheng, Zhiyuan Huang, Haochong China Univ Geosci Beijing Sch Sci Beijing 100083 Peoples R China

As an interferometric imaging method, digital holography has shown its unique potential in many fields, especially in the mature and diverse fields of crystallography. Compared to early microscopy imaging and X-ray diffraction approach, this technique captures and accurately reproduces the three-dimensional information of the crystal in real time. It offers advantages such as fast imaging, nondestructive testing, and optimized data processing. This review discusses the progress of digital holography in crystallography, covering crystallization, mineral imaging, and microstructure analysis of two-dimensional materials. The reconstruction of copper sulfate pentahydrate and sodium chloride crystallization serves as an example to demonstrate its powerful ability. Particular emphasis is placed on the advancement of optical instruments and the development of image reconstruction approaches. Regarding the solutions to problems such as dataset processing and field of view limitations, this paper summarizes the research results of combining digital holography with deep learning algorithm models and the free field of view method. In addition, the operating principle of the technology is expounded and the future development direction is also prospected.

关键词： crystallography deep learning digital holography mineral imaging

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DFALaneNet: A Dynamic real-time Lane Detection Network Based on Adaptive Scheduling

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HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2025年 15卷

作者： He, Wen Wei, Yu Ren, Fan Liu, Mingjie Chen, Yong Hao, Jinlong Li, Yan Univ Elect Sci & Technol China Sch Automat Engn Chengdu Peoples R China Changan Automot Software Technol Co Ltd Chongqing Peoples R China Chongqing Univ Posts & Telecommun Sch Automat Chongqing Peoples R China Inha Univ Dept Elect & Comp Engn Incheon South Korea

Lane detection is an important aspect of autonomous driving. For real-world applications, both accuracy and processing time are very important for this technology. Thus, this study proposed a dynamic real-time deep learning-based lane detection method based on the adaptive scheduling of input frames. An adaptive scheduling network was proposed to separate the input into key and adjacent frames based on the expected confidence. Consequently, an improved LaneNet encoder-decoder model was adopted as the primary image segmentation network, which was used to perform lane detection on a keyframe to ensure high accuracy and robustness. A neural optical flow network was used to perform lane predictions on adjacent frames to improve the speed of the detection network. We evaluated our proposed method on TuSimple, UCLane, and our campus datasets to demonstrate its effectiveness. The experimental results indicated that the proposed method performed well on both challenging public and self-made campus datasets, with substantial improvements compared with certain well-known methods.

关键词： Lane Detection image Segmentation Adaptive Scheduling Network

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Pashto script and graphics detection in camera captured Pashto document images using deep learning model

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2089-e2089页

作者： Bahadar, Khan Ahmad, Riaz Aurangzeb, Khursheed Muhammad, Siraj Ullah, Khalil Hussain, Ibrar Syed, Ikram Anwar, Muhammad Shahid Shaheed Benazir Bhutto Univ Dept Comp Sci Sheringal Pakistan King Saud Univ Coll Comp & Informat Sci Dept Comp Engn Riyadh Saudi Arabia Univ Malakand Dept Software Engn Chakdara Pakistan Gachon Univ Dept AI & Software Seongnam Si South Korea

Layout analysis is the main component of a typical Document image Analysis (DIA) system and plays an important role in pre-processing. However, regarding the Pashto language, the document images have not been explored so far. This research, for the first time, examines Pashto text along with graphics and proposes a deep learningbased classifier that can detect Pashto text and graphics per document. Another notable contribution of this research is the creation of a real dataset, which contains more than 1,000 images of the Pashto documents captured by a camera. For this dataset, we applied the convolution neural network (CNN) following a deep learning technique. Our intended method is based on the development of the advanced and classical variant of Faster R-CNN called Single-Shot Detector (SSD). The evaluation was performed by examining the 300 images from the test set. Through this way, we achieved a mean average precision (mAP) of 84.90%.

关键词： Script detection Graphic detection deep learning Document images

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real-time deep learning-Based Object Recognition in Augmented reality

Real-Time Deep Learning-Based Object Recognition in Augmente...

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Conference on real-time processing of image, Depth, and Video Information

作者： Egipko, V Zhdanova, M. Gapon, N. Voronin, V. Semenishchev, E. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia Don State Tech Univ Rostov Na Donu Russia

ISBN: (纸本)9781510673199;9781510673182

Augmented reality is a visualization technology that displays information by adding virtual images to the real world. Effective implementation of augmented reality requires recognition of the current scene. Identifying objects in real-time video on computationally limited hardware requires significant effort. One way to solve this problem is to create a hybrid system that, based on machine learning and computer vision technology, processes and analyzes visual data to identify and classify real-world objects. The proposed architecture is based on a combination of the Vuforia augmented system, which provides good performance by balancing prediction accuracy and efficiency. First, the Vuforia neural network architecture allows convenient interaction with AR in Unity and provides initial conditions for detecting 3D objects. The augmented reality construction algorithm is based on the ARCore framework and the OpenGL interface for embedded systems. The system integrates recognition data with an AR platform to display corresponding 3D models, allowing users to interact with them through the functionality of the AR application. This method also involves the development of an enhanced user interface for AR, making the augmented environment more accessible for navigation and control. Experimental research has shown that the proposed method significantly improves the accuracy of object recognition and the ease of working with 3D models in AR.

关键词： deep learning augmented reality computer vision object recognition robotic systems real-time processing

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Distributed photovoltaic ultra-short-term power prediction using whole-sky images and multi-source data

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ELECTRICAL ENGINEERING 2025年 1-19页

作者： Zhang, Qinlong Hou, Beiping Zhu, Wen Dong, Jianwei Yu, Aihua Zhu, Yuzhen Huang, Minyu Hu, Feiyang Zhejiang Univ Sci & Technol Sch Automat & Elect Engn Hangzhou 310023 Peoples R China Zhejiang Univ Sci & Technol Zhejiang Int Sci & Technol Cooperat Base Intellige Hangzhou 310023 Zhejiang Peoples R China

In recent years, photovoltaic (PV) power generation has received increasing attention. However, uncertainties in PV power output-especially the random variations caused by cloud cover-make accurate short-term forecasting essential for efficient power dispatch. This study proposes a novel ultra-short-term PV power forecasting framework that differs from previous methods by effectively integrating deep image features from ground-based sky images with multi-source meteorological and power generation data. Central to this approach is PatchDLinear, a precise and lightweight time series forecasting algorithm that improves upon traditional models by incorporating normalization and patch-based processing, which better captures local temporal patterns and trends. Initially, the Cloud Y-Net model is used to calculate cloud coverage and classify cloud types, extracting deep-level information from sky images. These features are then combined with meteorological and power generation data and fed into PatchDLinear for collaborative forecasting. Experiments using real-world images and data demonstrate that the improved module achieves RMSE enhancements of 5.10% and 7.84% over the original model structure, while the overall framework shows RMSE improvements ranging from 17.61 to 43.24% compared to baseline methods. These results confirm that PatchDLinear significantly enhances prediction accuracy, offering a robust solution for ultra-short-term PV power forecasting.

关键词： Photovoltaic deep learning time series forecasting image processing

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Application of intelligent real-time image processing in fitness motion detection under internet of things

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JOURNAL OF SUPERCOMPUTING 2022年第6期78卷 7788-7804页

作者： Cai, Hang Nanjing Normal Univ Sch Sports Sci & Phys Educ Nanjing 210000 Jiangsu Peoples R China

The purpose is to study the applicability of digital and intelligent real-time image processing (IP) in fitness motion detection under the environment of the Internet of Things (IoT). Given the absence of real-time training standards and possible workout injury problems during fitness activities, an intelligent fitness real-time IP system based on deep learning (DL) is implemented. Specifically, the keyframes of the real-time images are collected from the fitness monitoring video, and the DL algorithm is introduced to analyze the fitness motions. Afterward, the performance of the proposed system is evaluated through simulation. Subsequently, the Noise Reduction (NR) performance of the proposed algorithm is evaluated from the Peak Signal-to-Noise Ratio (PSNR), which remains above 20 dB for seriously noisy images (with a noise density reaching up to 90%). By comparison, the PSNR of the Standard Median Filter (SMF) and Ranked-order Based Adaptive Median Filter (RAMF) algorithms are not higher than 10 dB. Meanwhile, the proposed algorithm outperforms other DL algorithms by over 2.24% with a detection accuracy of 97.80%;the proposed system can adaptively detect the fitness motion, with a transmission delay no larger than 1 s given a maximum of 750 keyframes. Therefore, the proposed DL-based intelligent fitness real-time IP algorithm has strong robustness, high detection accuracy, and excellent real-time image diagnosis and processing effect, thus providing an experimental reference for sports digitalization and intellectualization.

关键词： deep learning real-time image processing Internet of Things Fitness motion Artificial intelligence

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image segmentation for blind lanes based on improved SegNet model

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JOURNAL OF ELECTRONIC IMAGING 2023年第1期32卷

作者： Xia, Yongquan Li, Yiqing Ye, Qianqian Dong, Jianhua Zhengzhou Univ Light Ind Sch Comp & Commun Engn Zhengzhou Peoples R China

The position of blind lanes must be correctly determined in order for blind people to travel safely. Aiming at the low accuracy and slow speed of traditional blind lanes image segmentation algorithms, a semantic segmentation method based on SegNet and MobileNetV3 is proposed. The main idea is to replace the coding part of the original SegNet model with the feature extraction part of MobileNetV3 and remove the pooling layer. Blind lanes images were collected through online search and self-shooting, and then the data were manually marked by LabelMe software and trained on TensorFlow deep learning framework. The experimental results show that the improved model has high segmentation accuracy and recognition speed. The pixel accuracy of blind lanes segmentation is 98.21%, the mean intersection over union is 96.29%, and the average time for processing a 416 x 416 image is 0.057 s, which meets the real-time requirements of the blind guidance system.

关键词： blind lanes segmentation image semantic segmentation deep learning image segmentation MobileNetV3 SegNet

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A deep Convolutional Encoder-Decoder-Restorer Architecture for image Deblurring

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NEURAL processing LETTERS 2024年第1期56卷 1-20页

作者： Fan, Yiqing Hong, Chaoqun Zeng, Guanghui Liu, Lijuan Xiamen Univ Technol Key Lab Fujian Univ Virtual Real & 3D Visualizat Xiamen 361024 Fujian Peoples R China

The accuracy of many computer vision tasks is reduced by blurred images, so deblur is important. More details of the image can be captured by a common multi-stage network, but the computational complexity of this method is higher compared with a single-stage network. However, a single-stage network cannot capture multi-scale information well. To tackle the problem, a novel convolutional encoder-decoder-restorer architecture is proposed. In this architecture, a multi-scale input structure is used in the encoder. Improved supervised attention module is inserted into the encoder for enhanced feature acquisition. In decoder, information supplement block is proposed to fuse multi-scale features. Finally, the fused features are used for image recovery in the restorer. In order to optimise the model in multiple domains, the loss function is calculated separately in the spatial and frequency domains. Our method is compared with existing methods on the GOPRO dataset. In addition, to verify the applications of our proposed method, we conduct experiments on the real image dataset, the VOC2007 dataset and the LFW dataset. Experimental results show that our proposed method outperforms state-of-the-art deblurring methods and improves the accuracy of different vision tasks.

关键词： image deblurring Encoder-decoder-restorer architecture Multi-Scale deep learning

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A Generative Neighborhood-Based deep Autoencoder for Robust Imbalanced Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 80-91页

作者： Troullinou, Eirini Tsagkatakis, Grigorios Losonczy, Attila Poirazi, Panayiota Tsakalides, Panagiotis University of Crete Department of Computer Science HeraklionGR 70013 Greece Institute of Computer Science - FORTH HeraklionGR 70013 Greece Columbia University Department of Neuroscience New YorkNY10027 United States Mortimer B. Zuckerman Mind Brain Behavior Institute New YorkNY10027 United States Foundation for Research and Technology Hellas Institute of Molecular Biology and Biotechnology Heraklion70013 Greece

deep learning models perform remarkably well on many classification tasks recently. The superior performance of deep neural networks relies on the large number of training data, which at the same time must have an equal class distribution in order to be efficient. However, in most real-world applications, the labeled data may be limited with high imbalance ratios among the classes, and thus, the learning process of most classification algorithms is adversely affected resulting in unstable predictions and low performance. Three main categories of approaches address the problem of imbalanced learning, i.e., data-level, algorithmic level, and hybrid methods, which combine the two aforementioned approaches. Data generative methods are typically based on generative adversarial networks, which require significant amounts of data, while model-level methods entail extensive domain expert knowledge to craft the learning objectives, thereby being less accessible for users without such knowledge. Moreover, the vast majority of these approaches are designed and applied to imaging applications, less to time series, and extremely rare to both of them. To address the above issues, we introduce GENDA, a generative neighborhood-based deep autoencoder, which is simple yet effective in its design and can be successfully applied to both image and time-series data. GENDA is based on learning latent representations that rely on the neighboring embedding space of the samples. Extensive experiments, conducted on a variety of widely-used real datasets demonstrate the efficacy of the proposed method. © 2020 IEEE.

关键词： time series analysis

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VELIE: A Vehicle-Based Efficient Low-Light image Enhancement Method for Intelligent Vehicles

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SENSORS 2024年第4期24卷 1345页

作者： Ye, Linwei Wang, Dong Yang, Dongyi Ma, Zhiyuan Zhang, Quan Univ Liverpool Dept Elect & Elect Engn Liverpool L69 3BX England Xian Jiaotong Liverpool Univ Sch Adv Technol 111 Renai Rd Suzhou 215123 Peoples R China Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA

In Advanced Driving Assistance Systems (ADAS), Automated Driving Systems (ADS), and Driver Assistance Systems (DAS), RGB camera sensors are extensively utilized for object detection, semantic segmentation, and object tracking. Despite their popularity due to low costs, RGB cameras exhibit weak robustness in complex environments, particularly underperforming in low-light conditions, which raises a significant concern. To address these challenges, multi-sensor fusion systems or specialized low-light cameras have been proposed, but their high costs render them unsuitable for widespread deployment. On the other hand, improvements in post-processing algorithms offer a more economical and effective solution. However, current research in low-light image enhancement still shows substantial gaps in detail enhancement on nighttime driving datasets and is characterized by high deployment costs, failing to achieve real-time inference and edge deployment. Therefore, this paper leverages the Swin Vision Transformer combined with a gamma transformation integrated U-Net for the decoupled enhancement of initial low-light inputs, proposing a deep learning enhancement network named Vehicle-based Efficient Low-light image Enhancement (VELIE). VELIE achieves state-of-the-art performance on various driving datasets with a processing time of only 0.19 s, significantly enhancing high-dimensional environmental perception tasks in low-light conditions.

关键词： autonomous driving low-light image enhancement RGB sensor image processing

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