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A GPU-based real-time processing.system for frequency division multiple-input-multiple-output radar

IET RADAR SONAR AND NAVIGATION 2023年第10期17卷 1524-1537页

作者： Liu, Gaogao Bao, Yuqian Yue, Ning Wang, Sitian Wu, Hui Liu, Qiang Yang, Wenbo Xidian Univ Sch Elect & Engn Xian Peoples R China

Multiple-Input-Multiple-Output (MIMO) radar has the characteristic of multiple antenna channels, bringing on a big data volume for signal processing. Therefore, the trade-off between detection ability and computational efficiency is always considered. In this article, an optimisation system is proposed to enhance the real-time performance of frequency division MIMO radar without compromising accuracy. For the scenario of low-altitude small target detection, a signal processing.acceleration method is proposed and a MIMO radar optimisation system based on graphics processing.unit (GPU) architecture is built. The signal model of frequency division MIMO radar is first established, improving the classical signal processing.flow efficiency from the perspective of reducing fast Fourier transform (FFT) times and windowing operation times. To achieve an advanced acceleration, the parallel architecture of ArrayFire-library in GPU is then employed. Distinct minimum parallel units are extracted by analysing the principle of digital beamforming (DBF), pulse compression, moving target detection (MTD), and constant false-alarm rate (CFAR) algorithms. And the corresponding parallel algorithms are designed to constitute a parallel acceleration system of frequency division MIMO. Simulation results indicate that the proposed method significantly improves the efficiency of MIMO system with a maximum acceleration ratio of 65 times, meeting the real-time processing.requirements.

关键词： array signal processing frequency-domain analysis MIMO radar optimisation parallel algorithms

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A novel approach to authentication of highbush and lowbush blueberry cultivars using image analysis, traditional machine learning and deep learning algorithms

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EUROPEAN FOOD RESEARCH AND TECHNOLOGY 2025年第2期251卷 193-204页

作者： Ropelewska, Ewa Koniarski, Michal Natl Inst Hort Res Fruit & Vegetable Storage & Proc Dept Konstytucji 3 Maja 1-3 PL-96100 Skierniewice Poland Natl Inst Hort Res Cultivar Testing Nursery & Gene Bank Resources Dep Konstytucji 3 Maja 1-3 PL-96100 Skierniewice Poland

The objective of this study was to classify blueberry cultivars based on image texture parameters using models built using traditional machine learning and deep learning algorithms. The blueberries belonging to highbush cultivars ('Bluecrop', 'Herbert', 'Jersey', and 'Nelson') and lowbush cultivars ('Emil' and 'Putte') were subjected to imaging using a digital camera. The texture parameters from blueberry images in color channels R, G, B, L, a, b, X, Y, Z, U, V, and S were determined. After selection image textures were used to build models for the classification of all highbush and lowbush blueberry cultivars, and highbush blueberry cultivars and lowbush blueberry cultivars, separately. In the case of distinguishing all cultivars, such as 'Bluecrop', 'Herbert', 'Jersey', and 'Nelson', 'Emil' and 'Putte', the classification accuracy reached 92.33% for a model built using a deep learning algorithm. Models built to distinguish only highbush cultivars provided an average accuracy of up to 91.25% (WiSARD). For models developed to classify two lowbush cultivars, an average accuracy reaching 96% (WiSARD) was found. The applied procedure can be used in practice to distinguish blueberry cultivars before their consumption or processing.

关键词： Highbush blueberry Lowbush blueberry image parameters Machine learning models

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digital image denoising by partial differential equation based on P-M model and its fuzzy evaluation method system 23

Digital image denoising by partial differential equation bas...

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2nd Asia Conference on algorithms, Computing and Machine Learning (CACML)

作者： Jingying, L. Yang, H. Minist Ind & Informat Technol Inst Elect 5 Beijing Peoples R China

ISBN: (纸本)9781450399449

Aiming at the problems of storage, batch migration and centralized processing.of visual digital images of infrared imaging products, this paper takes digital image noise reduction as the main research object and starts with the concept of image partial differential equation processing. Based on the development history, advantages, practicability and operability of digital image processing.by partial differential equation, it is concluded that digital image processing.technology based on P-M model method is more suitable for modern image processing. and also broadens and improves the basic algorithm of digital image processing.in the past. On this basis, the image quality is evaluated by using the fuzzy comprehensive evaluation theory based on analytic hierarchy process. The results show that the optimized processing.system can screen the advantages and disadvantages of visual digital images of infrared imaging products and provide technical support.

关键词： digital image Partial differential equation image denoising Fuzzy comprehensive evaluation Hierarchical analysis

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Re-UNet: a novel multi-scale reverse U-shape network architecture for low-dose CT image reconstruction

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2024年第3期62卷 701-712页

作者： Xiong, Lianjin Li, Ning Qiu, Wei Luo, Yiqian Li, Yishi Zhang, Yangsong Southwest Univ Sci & Technol Sch Comp Sci & Technol Lab Brain Sci & Med Artificial Intelligence Mianyang 621010 Peoples R China Chongqing Med Univ Dept Resp & Crit Care Med Affiliated Hosp 1 Chongqing 400016 Peoples R China Mianyang Cent Hosp NHC Key Lab Nucl Technol Med Transformat Mianyang 621000 Peoples R China Southwest Univ Sci & Technol Key Lab Testing Technol Mfg Proc Minist Educ Mianyang 621010 Peoples R China

In recent years, the growing awareness of public health has brought attention to low-dose computed tomography (LDCT) scans. However, the CT image generated in this way contains a lot of noise or artifacts, which make increasing researchers to investigate methods to enhance image quality. The advancement of deep learning technology has provided researchers with novel approaches to enhance the quality of LDCT images. In the past, numerous studies based on convolutional neural networks (CNN) have yielded remarkable results in LDCT image reconstruction. Nonetheless, they all tend to continue to design new networks based on the fixed network architecture of UNet shape, which also leads to more and more complex networks. In this paper, we proposed a novel network model with a reverse U-shape architecture for the noise reduction in the LDCT image reconstruction task. In the model, we further designed a novel multi-scale feature extractor and edge enhancement module that yields a positive impact on CT images to exhibit strong structural characteristics. Evaluated on a public dataset, the experimental results demonstrate that the proposed model outperforms the compared algorithms based on traditional U-shaped architecture in terms of preserving texture details and reducing noise, as demonstrated by achieving the highest PSNR, SSIM and RMSE value. This study may shed light on the reverse U-shaped network architecture for CT image reconstruction, and could investigate the potential on other medical image processing.

关键词： Low-dose CT UNet Deep learning image reconstruction

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Weakly Supervised Network with Scribble-Supervised and Edge-Mask for Road Extraction from High-Resolution Remote Sensing images

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Computers, Materials & Continua 2024年第4期79卷 549-562页

作者： Supeng Yu Fen Huang Chengcheng Fan College of Artificial Intelligence Nanjing Agricultural UniversityNanjing210095China Innovation Academy for Microsatellites of CAS Shanghai201210China Shanghai Engineering Center for Microsatellites Shanghai201210China Key Laboratory of Satellite Digital Technology Shanghai201210China

Significant advancements have been achieved in road surface extraction based on high-resolution remote sensingimage processing. Most current methods rely on fully supervised learning, which necessitates enormous humaneffort to label the image. Within this field, other research endeavors utilize weakly supervised methods. Theseapproaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such asscribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised andedge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equippedwith a distinct decoder module dedicated to road extraction tasks. One of the branches is dedicated to generatingedge masks using edge detection algorithms and optimizing road edge details. The other two branches supervise themodel’s training by employing scribble labels and spreading scribble information throughout the image. To addressthe historical flaw that created pseudo-labels that are not updated with network training, we use mixup to blendprediction results dynamically and continually update new pseudo-labels to steer network training. Our solutiondemonstrates efficient operation by simultaneously considering both edge-mask aid and dynamic pseudo-labelsupport. The studies are conducted on three separate road datasets, which consist primarily of high-resolutionremote-sensing satellite photos and drone images. The experimental findings suggest that our methodologyperforms better than advanced scribble-supervised approaches and specific traditional fully supervised methods.

关键词： Semantic segmentation road extraction weakly supervised learning scribble supervision remote sensing image

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SDAT: Sub-Dataset Alternation Training for Improved image Demosaicing

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF SIGNAL processing 2024年 5卷 611-620页

作者： Becker, Yuval Nossek, Raz Z. Peleg, Tomer SIRC Samsung Israel R&D Ctr IL-6492103 Tel Aviv Israel

image demosaicing is an important step in the image processing.pipeline for digital cameras. In data centric approaches, such as deep learning, the distribution of the dataset used for training can impose a bias on the networks' outcome. For example, in natural images most patches are smooth, and high-content patches are much rarer. This can lead to a bias in the performance of demosaicing algorithms. Most deep learning approaches address this challenge by utilizing specific losses or designing special network architectures. We propose a novel approach SDAT, Sub-Dataset Alternation Training, that tackles the problem from a training protocol perspective. SDAT is comprised of two essential phases. In the initial phase, we employ a method to create sub-datasets from the entire dataset, each inducing a distinct bias. The subsequent phase involves an alternating training process, which uses the derived sub-datasets in addition to training also on the entire dataset. SDAT can be applied regardless of the chosen architecture as demonstrated by various experiments we conducted for the demosaicing task. The experiments are performed across a range of architecture sizes and types, namely CNNs and transformers. We show improved performance in all cases. We are also able to achieve state-of-the-art results on three highly popular image demosaicing benchmarks.

关键词： Training Task analysis image restoration Convergence image reconstruction image color analysis Benchmark testing Demosaicing image restoration inductive bias

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Design of area, energy and security optimized reversible architectures for digital image cryptography

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CIRCUITS SYSTEMS AND SIGNAL processing 2023年第9期42卷 5358-5384页

作者： Saranya, K. Vijeyakumar, K. N. Dr Mahalingam Coll Engn & Technol Dept Elect & Elect Engn Coimbatore 642003 Tamil Nadu India Dr Mahalingam Coll Engn & Technol Dept Elect & Commun Engn Coimbatore 642003 Tamil Nadu India

image data is frequently processed in multimedia, wireless, mobile communication etc. The digital images transmitted over the internet are prone to security threats and thus providing security for the images and videos becomes an essential issue for individuals, business and governments. Moreover, applications in the automobile, banking, medical, construction and fashion industry require designs, scanned data, and blue-prints to be protected against attackers. Though many cryptographic algorithms are proposed in literatures, there is no much effort concentrated on the design of low-power hardware-efficient cryptosystem architectures. In this work, we propose a novel crypto architecture using multiple Boolean reversible blocks with logic control inputs derived from standard test images. The pre-processing.and shuffling on the control pixel bits decide the depth of logic operation of the crypto system. The proposed design involves Gray Code image Scrambling block for pre-processing. Reversible Shuffling Unit for bit plane modification and final merger for plane regrouping and is designated as Reversible Logic-image Key (RL-IK) design. The use of reversible logic unit for shuffling combined with Gray code image scrambling in key generation improves the security of the processed images from the encryption unit at the expense of increase in area. To optimize the proposed RL-IK design in terms of area and security metrics, Gray Code bit plane processing.is eliminated in the least n/2-1 bits as the contribution of least significant bits in the pixel intensity is less. The proposed Area and Security Optimized RL-IK architecture (AS-RL-IK) involves same process as that of its standard counterpart in n/2 + 1 Most Significant Bits bits. As the weight based shuffling block is the critical unit of the proposed architecture that contributes for high delay and hardware area, for portable non-critical applications high speed hardware efficient variant of RL-IK is proposed. The prop

关键词： Reversible logic image key Gray code image scrambling Reversible shuffling image encryption

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An improved PRNU noise extraction model for highly compressed image blocks with low resolutions

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第25期83卷 66657-66690页

作者： Tian, Nili Qiu, Xian Pan, Qing Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China

Detecting sources in digital images and videos is crucial to multimedia forensics research. The inherent physical properties of imaging sensors result in the presence of Photo Response Non-Uniformity Noise (PRNU) within the captured multimedia content. This particular noise, often called the "fingerprint," is a unique and stable feature for identifying the source camera. However, the compression of images by platform codecs on social network media introduces varying degrees of quantization noise, making to address this issue, a noise extractor based on variance stabilizing transform and adaptive block clustering principal component analysis (PCA) is proposed, along with an enhanced processing.model that incorporates cyclic residual recycling. Firstly, the GAT and adaptive block clustering PCA filtering are applied to extract noises from the images. The obtained noises are then subjected to zero-mean and diagonal artifact elimination processing. Next, the real and imaginary parts of the noise spectrum are individually subjected to real-time iterative least squares smoothing based on half-quadratic optimization. Due to the presence of PRNU information in the residual between the pre-smoothed and post-smoothed signals, additional cyclic smoothing is applied to refine the signal further. Finally, the smoothed signals are accumulated to obtain the enhanced PRNU noise. Experimental comparisons conducted on the public dataset Dresden demonstrate that the proposed model significantly outperforms existing methods in terms of source camera identification for low-resolution and strong JPEG compression images.

关键词： image forensics Source camera identification Photo response non-uniformity image processing.algorithms Compressed image identification

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A method to predict drilling-induced burr occurrence in chopped carbon fibre reinforced polymer (CFRP) composites based on digital image processing

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COMPOSITES PART B-ENGINEERING 2022年 242卷

作者： Geier, Norbert Poka, Gyorgy Jacso, Adam Pereszlai, Csongor Budapest Univ Technol & Econ Fac Mech Engn Dept Mfg Sci & Engn Muegyetem Rkp 3 H-1111 Budapest Hungary

Mechanical drilling-induced burr in carbon fibre reinforced polymer (CFRP) composites is one of the most sig-nificant macro-geometrical failures of CFRP composites;nevertheless, burr prediction in quasi-randomly oriented chopped fibre reinforced composites is not supported yet. To explore this issue, the main aim of the present research work was to develop a method to predict drilling-induced burrs in chopped CFRPs based on digital image processing. First, an indexable light source captured digital images of a chopped CFRP plate in different lighting conditions. Then, the fibre orientation of each visible chopped fibre group was determined in each image through digital image processing.algorithms. These images were then associated based on the superposition principle. Finally, the burr-dangerous regions were predicted by the local properties of chopped fibres. The prediction accuracy of the algorithm is tested by drilling experiments in chopped CFRP plates using solid carbide drills. The experimental results show that the mechanical drilling-induced burr prediction accuracy is 64-97%. By applying the proposed method, burrs can be estimated without machining experiments in chopped CFRPs.

关键词： Polymer-matrix composites (PMCs) Defects Directional orientation Machining

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Visible and near-infrared image fusion based on information complementarity

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CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2025年第1期10卷 193-206页

作者： Li, Zhuo Pu, Shiliang Ji, Mengqi Zeng, Feng Li, Bo Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Hangzhou Hikvis Digital Technol Co Ltd Hangzhou Peoples R China Beihang Univ Sch Artificial Intelligence Beijing Peoples R China Beihang Univ Hangzhou Int Innovat Inst Hangzhou Peoples R China

images with complementary spectral information can be recorded using image sensors that can identify visible and near-infrared spectrum. The fusion of visible and near-infrared (NIR) aims to enhance the quality of images acquired by video monitoring systems for the ease of user observation and data processing. Unfortunately, current fusion algorithms produce artefacts and colour distortion since they cannot make use of spectrum properties and are lacking in information complementarity. Therefore, an information complementarity fusion (ICF) model is designed based on physical signals. In order to separate high-frequency noise from important information in distinct frequency layers, the authors first extracted texture-scale and edge-scale layers using a two-scale filter. Second, the difference map between visible and near-infrared was filtered using the extended-DoG filter to produce the initial visible-NIR complementary weight map. Then, to generate a guide map, the near-infrared image with night adjustment was processed as well. The final complementarity weight map was subsequently derived via an arctanI function mapping using the guide map and the initial weight maps. Finally, fusion images were generated with the complementarity weight maps. The experimental results demonstrate that the proposed approach outperforms the state-of-the-art in both avoiding artificial colours as well as effectively utilising information complementarity.

关键词： image denoising image enhancement image sensors near-infrared image processing

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