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2024 International Conference on image processing

作者： Yang, Qijun Yin, Hujun Univ Manchester Dept Elect & Elect Engn Manchester Lancs England

ISBN: (纸本)9798350349405;9798350349399

We propose a solution to Fourier ptychographic microscopy (FPM) by combining a no-reference image quality assessment module based on information entropy (IENR-IQA) and a physics-based neural network (PbNN) to achieve rapid reconstruction from multiple low-resolution images to high-resolution images. This improves the reconstruction and makes it more generalizable and robust than the traditional FPM methods. Existing reconstruction methodologies are susceptible to systematic errors, including pupil aberration and light-emitting diode (LED) positional intensity discrepancies, which profoundly impact reconstruction clarity and color fidelity. In response, a PbNN featuring image quality evaluation is introduced for FPM reconstruction in this paper. A key innovation is incorporating the IENR-IQA module within a PbNN, to facilitate adaptive correction of spatial variations in LED intensity and position. In addition, the IENR-IQA module employs a fully connected layer to rectify pupil aberration. Rigorous simulations and experiments were conducted to validate the proposed method's effectiveness and robustness. Experimental results demonstrate that the proposed IENR-IQA model can predict image quality well. When combined with PbNN in FPM, the image quality is improved compared to the existing physical neural networks, making the reconstruction results closer to human vision perception. The proposed IENR-IQA PbNN enhances the possibilities for applying the FPM technology in practice.

关键词： Fourier ptychographic microscopy (FPM) no-reference image quality assessment module based on information entropy (IENR-IQA) physics-based neural network (PbNN)

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Deep hash image retrieval based on multi-head attention mechanism 24

Deep hash image retrieval based on multi-head attention mech...

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2024 International Conference on Virtual Reality, image and signal processing, ICVISP 2024

作者： Yang, Jing Zhang, Sengbao Wang, Tianlin Gao, Shang Jiangsu University of Science and Technology Zhenjiang China

ISBN: (纸本)9798400710926

In the Internet era, massive amounts of image data have em erged. How to efficiently perform accurate and efficient image retrieval from this data has become a crucial issue. Most methods are affected by the redundant information in the extracted features and And it cannot extract effective information from the image, resulting in a decrease in image retrieval performance and accuracy. To address this challenge, we introduce a deep hashing algorithm that integrates the multi-head attention mechanism. This mechanism is incorporated within a convolutional neural network, allowing the hash network to adaptively learn and focus on features from various distributions within images. Furthermore, we have redefined the hash loss function to enhance the effectiveness and expressive capability of hash encoding in feature representation. Extensive experiments conducted on CIFAR-10 and NUS-WIDE datasets demonstrate that our proposed method achieves superior performance compared to existing approaches. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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image defogging based on multi-input and multi-scale UNet

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signal image AND VIDEO processing 2023年第4期17卷 1143-1151页

作者： Lin, Zhengchun Luo, Qingxing Jiang, Yunzhi Wang, Jing Li, Siyuan Cheng, Gongwen Genrang, Zheng Guangdong Polytech Normal Univ Guangzhou 510000 Peoples R China Guangdong Prov Key Lab Intellectual Property & Bi Guangzhou 510000 Peoples R China Shenzhen Chace Network Informat Technol Co Ltd Shenzhen 518000 Peoples R China Zhongshan Polytech Zhongshan 528400 Peoples R China

The coarse-to-fine image defogging strategy has been widely used in the structural design of individual image defogging networks. In the traditional method, multi-scale input image subnets are superimposed, so that the sharpness of the image is gradually improved from the bottom subnet to the top subnet, which inevitably leads to the loss of image details. Toward a fast and accurate dehazing network design, we revisit the coarse-to-fine strategy and present a multi-input and multi-scale U-Net (MIMS-UNet). The MIMS-UNet has two distinct features. On the one hand, the single-encoder of MIMS-UNet adopts multi-input and multi-scale image, which increases the computation amount but greatly improves the network performance. On the other hand, codec structures with context blocks are used to capture context information and recover more details. The experimental results show that the proposed method achieves good results in both quantification and visualization. Compared with the existing methods, the proposed network can achieve ideal results of defogging and effectively avoid color distortion after defogging.

关键词： image dehazing Convolutional neural network Feature fusion image restoration image processing

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DRLSU-Net: Level set with U-Net for medical image segmentation

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DIGITAL signal processing 2025年 157卷

作者： Wang, Xiaofeng Liu, Jiashan Yang, Rentao Wu, Zhize Sun, Lingma Zou, Le Hefei Univ Sch Artificial Intelligence & Big Data Hefei 230601 Anhui Peoples R China

Convolutional neural networks (CNN) have been extensively utilized for image segmentation tasks, with the UNet architecture emerging as a classical model in medical imaging due to its simple structure and high scalability. However, for complex medical images, particularly those with blurred lesion boundaries, the U-Net model often loses significant edge information during feature extraction. Each layer in the encoder section is convolved with a simple stack of equal design, which is clearly not able to obtain sufficient feature information from an otherwise low-quality image. In order to solve this problem, the DRLSU-Net model is proposed which combines an enhanced U-Net architecture with distance regularized level set evolution (DRLSE). The DRLSU-Net model takes the results of U-Net pre-segmentation as an intermediate medium, combines the U-Net model with the level set method. Indirectly represent the target contour through the zero level set to obtain a more intuitive target edge location. Specifically, the Parallel Dilated Convolutional Sequence (PDCS) is introduced in the U-Net encoder to minimize information loss during down-sampling, and preserve more edge details. Secondly, the Mixed Attention Mechanism (MAM) is introduced into the decoder, aiding the network in recovering important information during image reconstruction, thus generating a more accurate output sequence. Finally, the pre-segmentation label mapping is converted into a level set function representation, which serves as a priori information for the level set method. A new energy functional is constructed to guide the evolution of the level set curves, helping to obtain a clear contour boundary. The performance of the DRLSU-Net model is evaluated on the ISIC2017, ISIC2018, CVC-ClinicDB, and Lung datasets. Extensive experiment results show better performance than other state-of-the-art (SOTA) methods in terms of mIoU and F1-socre, and the results indicate that the DRLSU-Net model performs competit

关键词： Convolutional neural network image segmentation U-Net Level set

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Multi sensor moving image fusion analysis algorithm on the basis of neural network technology

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JOURNAL OF COMPUTATIONAL methods IN SCIENCES AND ENGINEERING 2023年第3期23卷 1209-1224页

作者： Zhan, Keqiang Fujian Polytech Informat Technol Internet Things & Artificial Intelligence Coll Fuzhou 350003 Fujian Peoples R China

image fusion can extract the useful information of each channel about the same target to the maximum extent, and get high quality image. However, in this process, the image quality may be affected by noise and reduced. To reduce the image noise's influence on the image fusion effect as well as improve the image fusion quality, a multi sensor moving image fusion analysis algorithm on the basis of neural network technology is proposed. This study designed a window adaptive strategy, use the probability density function, and built an impulse noise model, and use this model to divide each pixel in the image into noise points or signal points to obtain image impulse noise detection results, and use bilateral filtering algorithm to achieve image denoising processing;The fruit fly optimization algorithm is adopted to detect the edge of the multi sensor moving image, extract the image's main edge points, and remove the detail edge points and noise points;nonlinear convolutional layer is used to replace most fusion layers to improve the dense network model, and the cross-entropy loss is used as the loss function in training the network, then use guided filters to generate guide maps, and generate final fusion images. According to experimental results, the noise detection method in this paper can also maintain 79.21% non-noise extraction rate under the noise density of 0.7. The highest correlation coefficient between the proposed algorithm and the standard image is 37.41. Its peak signal-to-noise ratio is as low as 0.09 and as high as 0.52. It has a minimum root mean square error of 8.52. The above values are better than other measured methods, and its edge miss rate can be as low as 1%, the image resolution is higher. It can be seen that its image denoising effect is better. image denoising effect, and low edge missed detection rate, which effectively improves the effect of image fusion.

关键词： neural network technology multi sensor image fusion impulse noise model fruit fly optimization algorithm improved dense network

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INDUCTIVE GRAPH neural NETWORKS FOR MOVING OBJECT SEGMENTATION 30

INDUCTIVE GRAPH NEURAL NETWORKS FOR MOVING OBJECT SEGMENTATI...

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30th IEEE International Conference on image processing (ICIP)

作者： Prummel, Wieke Giraldo, Jhony H. Zakharova, Anastasia Bouwmans, Thierry La Rochelle Univ Lab Math Image & Applicat MIA La Rochelle France Inst Polytech Paris Telecom Paris LTCI Paris France

ISBN: (纸本)9781728198354

Moving Object Segmentation (MOS) is a challenging problem in computer vision, particularly in scenarios with dynamic backgrounds, abrupt lighting changes, shadows, camouflage, and moving cameras. While graph-based methods have shown promising results in MOS, they have mainly relied on transductive learning which assumes access to the entire training and testing data for evaluation. However, this assumption is not realistic in real-world applications where the system needs to handle new data during deployment. In this paper, we propose a novel Graph Inductive Moving Object Segmentation (GraphIMOS) algorithm based on a Graph neural Network (GNN) architecture. Our approach builds a generic model capable of performing prediction on newly added data frames using the already trained model. GraphIMOS outperforms previous inductive learning methods and is more generic than previous transductive techniques. Our proposed algorithm enables the deployment of graph-based MOS models in real-world applications.

关键词： Moving object segmentation graph neural networks inductive learning graph signal processing

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A multi-view image fusion algorithm for industrial weld

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IET image processing 2023年第1期17卷 193-203页

作者： Zheng, Qingchun Zhao, Yangyang Zhang, Xu Zhu, Peihao Ma, Wenpeng Tianjin Univ Technol Sch Comp Sci & Engn Tianjin 300384 Peoples R China Tianjin Univ Technol Sch Mech Engn Tianjin Key Lab Adv Mechatron Syst Design & Intel Tianjin Peoples R China Tianjin Univ Technol Natl Demonstrat Ctr Expt Mech & Elect Engn Educ Tianjin Peoples R China

Multi-view image fusion can be used to extract features from redundant and complementary multisource images. And the technique of obtaining high quality fusion images has become one of the research hotspots for image processing. In order to realize defect detection and intelligent grinding smoothly, multi-view fusion technology was applied in the field of overexposure and underexposure industrial welds, achieving high quality image enhancement. When preparing the data set of multi-view images, a hybrid registration algorithm with high matching ability is proposed. The data set of overexposure and underexposure weld images was obtained successfully by using the registration algorithm. In order to improve the fusion ability of overexposure and underexposure industrial welds, we propose a novel multi-view image fusion algorithm based on deep learning. The multi-view fusion algorithm uses an autoencoder network structure, and its innovation lies in a parallel branch network with lightweight structure and strong generalization ability. The experimental results demonstrate that compared with other classical multi-view algorithms, our proposed algorithm gets the best parameters on the industrial weld data set in peak signal to noise ratio (PSNR) and root mean square error (RMSE), reaching 59.12 and 0.084, respectively. And the ablation and performance comparison experiments verify that the proposed parallel branch network has better generalization ability and fusion accuracy than other classical multi branch networks.

关键词： classical multibranch networks feature extraction data set high quality fusion images Sensor fusion image enhancement underexposure weld images multiview images classical multiview algorithms generalization ability parallel branch network lightweight structures multiview fusion technology hybrid registration algorithm overexposure welds grinding neural nets industrial weld data fusion ability Optical, image and video signal processing image fusion Computer vision and image processing techniques novel multiview image fusion algorithm high quality image enhancement deep learning (artificial intelligence) image processing underexposure industrial welds multiview fusion algorithm overexposure weld images fusion accuracy image registration high matching ability Interpolation and function approximation (numerical analysis) mean square error methods complementary multisource images image resolution geophysical image processing

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Embedded deep learning models for multilingual speech recognition

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Rahmouni, Mohamed Hedi Salhi, Mohamed Salah Touti, Ezzeddine Allagui, Hatem Aoudia, Mouloud Barr, Mohammad Univ Tunis El Manar Fac Math Phys & Nat Sci Tunis Res Lab LAPER Tunis Tunisia Natl Engn Sch Tunis Res Lab Signal Image & Informat Technol LR SITI Tunis Tunisia Northern Border Univ Ctr Sci Res & Entrepreneurship Ar Ar 73213 Saudi Arabia Northern Border Univ Coll Engn Dept Ind Engn Ar Ar 73213 Saudi Arabia Northern Border Univ Coll Engn Dept Elect Engn Ar Ar 73213 Saudi Arabia

This paper investigates the hybridization of Genetic Algorithms (GA) with Recurrent Self- Organizing Maps (RSOM) for speech recognition. It ensures the benchmarking of its performance against traditional and deep learning-based methods, including Hidden Markov Models (HMM), Support Vector Machines (SVM), Convolutional neural Networks (CNN), Recurrent neural Networks (RNN), Long Short-Term Memory networks (LSTM), Gated Recurrent Units (GRU), and wave to vector 2.0 (wav2vec 2.0). The aim of this study is to demonstrate the performance of the hybrid GA-RSOM model implemented on an embedded system, such as a modern Digital signal processing (DSP). The evaluation is carried out in terms of reaction time and recognition accuracy for speech with very high variability and multilingual content. Experiments show that while the GARSOM model is slower than some models like CNN, it achieves a stable and precise recognition rate of up to 98 %, depending on the phonemes.

关键词： Features extraction Multilingual speech recognition Embedded systems Hybridized GA-RSOM Deep learning models

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HAU-Net: Hybrid CNN-transformer for breast ultrasound image segmentation

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BIOMEDICAL signal processing AND CONTROL 2024年 87卷

作者： Zhang, Huaikun Lian, Jing Yi, Zetong Wu, Ruichao Lu, Xiangyu Ma, Pei Ma, Yide Lanzhou Univ Sch Informat Sci & Engn Lanzhou Gansu Peoples R China Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou Gansu Peoples R China

Breast cancer is a significant health concern that remains one of the leading causes of mortality in women worldwide. Convolutional neural Networks (CNNs) have been shown to be effective in ultrasound breast image segmentation. Yet, because of the lack of long-distance dependence, the segmentation performance of CNNs is limited in addressing challenges typical of segmentation of ultrasound breast lesions, such as similar intensity distributions, the presence of irregular objects, and blurred boundaries. In order to overcome these issues, several studies have combined transformers and CNNs, to compensate for the shortcomings of CNNs with the ability of transformers to exploit long-distance dependence. Most of these studies limited themselves to rigidly plug transformer blocks into the CNN, lacking consistency in the process of feature extraction and therefore leading to poor performances in segmenting challenging medical images. In this paper, we propose HAU-Net(hierarchical attention-guided U-Net), a hybrid CNN-transformer framework that benefits from both the long-range dependency of transformers and the local detail representation of CNNs. To incorporate global context information, we introduce a L-G transformer block nested into the skip connections of the U shape architecture network. In addition, to further improve the segmentation performance, we added a cross attention block (CAB) module on the decoder side to allow different layers to interact. Extensive experimental results on three public datasets indicate that the proposed HAU-Net can achieve better performance than other state-of-the-art methods for breast lesions segmentation, with Dice coefficient of 83.11% for BUSI, 88.73% for UDIAT, and 89.48% for BLUI respectively.

关键词： Convolutional neural Network Transformer Breast lesions segmentation Ultrasound image

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MFNet: Panoptic segmentation network based on multiscale feature weighted fusion and frequency domain attention mechanism

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IET COMPUTER VISION 2023年第1期17卷 88-97页

作者： Lei, Haiwei He, Fangyuan Jia, Bohui Wu, Qian North Univ China Sch Data Sci & Technol Taiyuan Peoples R China

Existing panoptic segmentation networks based on multiscale methods do not distinguish shared features according to different scales, which leads to a loss of feature information and suboptimal results. To solve this problem, the authors improve on UPSNet (a unified panoptic segmentation network) by introducing a multiscale fusion module cascaded between layers into the semantic segmentation head of UPSNet. On the one hand, this enriches the feature information for differently scaled features by cascading the feature information of adjacent scales, and on the other hand, the feature information at each scale can be combined adaptively to achieve a better segmentation effect. Furthermore, a frequency domain attention mechanism is introduced into the backbone network of UPSNet to improve the feature extraction ability of the backbone network by learning more frequency domain feature information. The experimental results of the authors' improved network on the Common Objects in Context (COCO) and Cityscapes data sets show that compared with UPSNet, the performance is significantly improved: the panoptic quality is improved by 0.9% and 1.6%, respectively.

关键词： differently scaled features image segmentation feature extraction unified panoptic segmentation network shared features authors frequency domain feature information multiscale fusion module multiscale methods neural nets UPSNet multiscale feature weighted fusion Computer vision and image processing techniques Optical, image and video signal processing convolutional neural nets segmentation effect semantic segmentation head feature extraction ability panoptic segmentation networks backbone network frequency domain attention mechanism

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