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Multi-focus image fusion based on fully convolutional networks Research Articles

Rui Guo, Xuan-jing Shen, Xiao-yu Dong, Xiao-li Zhang,zhangxiaoli@jlu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-1118 doi: 10.1631/FITEE.1900336

Abstract: We propose a method, in which a fully convolutional network for focus detection (FD-FCN) is constructed. To obtain more precise focus detection maps, we propose to add s in the network to make both detailed and abstract visual information available when using FD-FCN to generate maps. A new training dataset for the proposed network is constructed based on dataset CIFAR-10. The image fusion algorithm using FD-FCN contains three steps: focus maps are obtained using FD-FCN, decision map generation occurs by applying a morphological process on the focus maps, and image fusion occurs using a decision map. We carry out several sets of experiments, and both subjective and objective assessments demonstrate the superiority of the proposed fusion method to state-of-the-art algorithms.

Keywords： 多焦距图像融合；全卷积网络；跳层；性能评估    

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Multi-focus image fusion based on fractional-order derivative and intuitionistic fuzzy sets Research Articles

Xue-feng Zhang, Hui Yan, Hao He,zhangxuefeng@mail.neu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6,   Pages 809-962 doi: 10.1631/FITEE.1900737

Abstract: Multi-focus is an increasingly important component in , and it plays a key role in imaging. In this paper, we put forward a novel multi-focus method which employs and . The original image is decomposed into a base layer and a detail layer. Furthermore, a new fractional-order spatial frequency is built to reflect the clarity of the image. The fractional-order spatial frequency is used as a rule for detail layers fusion, and are introduced to fuse base layers. Experimental results demonstrate that the proposed fusion method outperforms the state-of-the-art methods for multi-focus .

Keywords： 像融合；分数阶导数；直觉模糊集；多聚焦图像    

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Dual-constraint burst image denoising method Research Articles

Dan ZHANG, Lei ZHAO, Duanqing XU, Dongming LU,cszhd@zju.edu.cn,cszhl@zju.edu.cn,xdq@zju.edu.cn,ldm@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 2,   Pages 220-233 doi: 10.1631/FITEE.2000353

Abstract: has proven to be an effective mechanism for computer vision tasks, especially for and burst . In this paper, we focus on solving the burst problem and aim to generate a single clean image from a burst of noisy images. We propose to combine the power of block matching and 3D filtering (BM3D) and a convolutional neural network (CNN) for burst . In particular, we design a CNN with a divide-and-conquer strategy. First, we employ BM3D to preprocess the noisy burst images. Then, the preprocessed images and noisy images are fed separately into two parallel CNN branches. The two branches produce somewhat different results. Finally, we use a light CNN block to combine the two outputs. In addition, we improve the performance by optimizing the two branches using two different constraints: a signal constraint and a noise constraint. One maps a clean signal, and the other maps the noise distribution. In addition, we adopt block matching in the network to avoid frame misalignment. Experimental results on synthetic and real noisy images show that our algorithm is competitive with other algorithms.

Keywords： Image denoising     Burst image denoising     Deep learning    

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An efficient counter-based Wallace-tree multiplier with a hybrid full adder core for image blending Research Articles

Ayoub SADEGHI, Nabiollah SHIRI, Mahmood RAFIEE, Mahsa TAHGHIGH

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 6,   Pages 950-965 doi: 10.1631/FITEE.2100432

Abstract:

We present a new -based Wallace-tree (CBW) 8×8 . The ‍’‍s s are implemented with a new hybrid (FA) cell, which is based on the (TG) technique. The proposed FA, TG-based AND gate, and hybrid half adder (HA) generate :3 (4≤≤7) digital s with the ability to save at least 50% area occupation. Simulations by 90 nm technology prove the superiority of the proposed FA and digital s under different conditions over the state-of-the-art designs. By using the proposed cells, the CBW exhibits high driving capability, low power consumption, and high speed. The CBW has a 0.0147 mm die area in a pad. The post-layout extraction proves the accuracy of experimental implementation. An mechanism is proposed, in which a direct interface between MATLAB and HSPICE is used to evaluate the presented CBW in image processing applications. The peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM) are calculated as image quality parameters, and the results confirm that the presented CBW can be used as an alternative to designs in the literature.

Keywords： Full adder     Transmission gate     Counter     Multiplier     Three-dimensional layout     Image blending    

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Amultimodal dense convolution network for blind image quality assessment Research Article

Nandhini CHOCKALINGAM, Brindha MURUGAN

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1601-1615 doi: 10.1631/FITEE.2200534

Abstract: Technological advancements continue to expand the communications industry’s potential. Images, which are an important component in strengthening communication, are widely available. Therefore, image quality assessment (IQA) is critical in improving content delivered to end users. Convolutional neural networks (CNNs) used in IQA face two common challenges. One issue is that these methods fail to provide the best representation of the image. The other issue is that the models have a large number of parameters, which easily leads to overfitting. To address these issues, the dense convolution network (DSC-Net), a model with fewer parameters, is proposed for . Moreover, it is obvious that the use of multimodal data for has improved the performance of applications. As a result, fuses the texture features extracted using the gray-level co-occurrence matrix (GLCM) method and spatial features extracted using DSC-Net and predicts the image quality. The performance of the proposed framework on the benchmark synthetic datasets LIVE, TID2013, and KADID-10k demonstrates that the MDSC-Net approach achieves good performance over state-of-the-art methods for the NR-IQA task.

Keywords： No-reference image quality assessment (NR-IQA)     Blind image quality assessment     Multimodal dense convolution network (MDSC-Net)     Deep learning     Visual quality     Perceptual quality    

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Recent advances in multisensor multitarget tracking using random finite set Review Articles

Kai Da, Tiancheng Li, Yongfeng Zhu, Hongqi Fan, Qiang Fu,dktm131@163.com,t.c.li@nwpu.edu.cn,zoyofo@163.com,fanhongqi@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 1,   Pages 1-140 doi: 10.1631/FITEE.2000266

Abstract: In this study, we provide an overview of recent advances in multisensor based on the (RFS) approach. The fusion that plays a fundamental role in multisensor filtering is classified into data-level multitarget measurement fusion and estimate-level multitarget density fusion, which share and fuse local measurements and posterior densities between sensors, respectively. Important properties of each fusion rule including the optimality and sub-optimality are presented. In particular, two robust multitarget density-averaging approaches, arithmetic- and geometric-, are addressed in detail for various RFSs. Relevant research topics and remaining challenges are highlighted.

Keywords： Multitarget tracking     Multisensor fusion     Average fusion     Random finite set     Optimal fusion    

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Cooperative planning of multi-agent systems based on task-oriented knowledge fusion with graph neural networks Research Article

Hanqi DAI, Weining LU, Xianglong LI, Jun YANG, Deshan MENG, Yanze LIU, Bin LIANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1069-1076 doi: 10.1631/FITEE.2100597

Abstract: is one of the critical problems in the field of gaming. This work focuses on when each agent has only a local observation range and local communication. We propose a novel architecture that combines a graph neural network with a sampling method. Two main contributions of this paper are based on the comparisons with previous work: (1) we realize feasible and dynamic adjacent information fusion using (i.e., Graph SAmple and aggreGatE), which is the first time this method has been used to deal with the problem, and (2) a task-oriented sampling method is proposed to aggregate the available knowledge from a particular orientation, to obtain an effective and stable training process in our model. Experimental results demonstrate the good performance of our proposed method.

Keywords： Multi-agent system     Cooperative planning     GraphSAGE     Task-oriented knowledge fusion    

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The Application of Two Dimensions Wavelet in Image Edges Detection

Zhang Hongyan,Zhang Dengpan

Strategic Study of CAE 2003, Volume 5, Issue 4,   Pages 61-64

Abstract:

Edges as the main characterization of image vision have been throught as the main content in obtaining image information. Wavelet transform has the capacity for detecting local signal mutation and detects information using multiscale character, so it is taken as the excellent tool to detect the edges of the image information. This paper analyzes the basic theory using two dimensions wavelet to detect image edges on the basis of wavelet transform and then designs the detecting algorithm of the multi-scale edge matching. On the basis of researching results,the application programme is made to analyse the true examples.

Keywords： wavelet transform     multi-scale     edges detection    

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Co-Estimation of State of Charge and Capacity for Lithium-Ion Batteries with Multi-Stage Model Fusion Method Article

Rui Xiong, Ju Wang, Weixiang Shen, Jinpeng Tian, Hao Mu

Engineering 2021, Volume 7, Issue 10,   Pages 1471-1484 doi: 10.1016/j.eng.2020.10.022

Abstract:

Lithium-ion batteries (LIBs) have emerged as the preferred energy storage systems for various types of electric transports, including electric vehicles, electric boats, electric trains, and electric airplanes. The energy management of LIBs in electric transports for all-climate and long-life operation requires the accurate estimation of state of charge (SOC) and capacity in real-time. This study proposes a multistage
model fusion algorithm to co-estimate SOC and capacity. Firstly, based on the assumption of a normal distribution, the mean and variance of the residual error from the model at different ageing levels are used to calculate the weight for the establishment of a fusion model with stable parameters. Secondly, a differential error gain with forward-looking ability is introduced into a proportional–integral observer
(PIO) to accelerate convergence speed. Thirdly, a fusion algorithm is developed by combining a multistage model and proportional–integral–differential observer (PIDO) to co-estimate SOC and capacity under a complex application environment. Fourthly, the convergence and anti-noise performance of the fusion algorithm are discussed. Finally, the hardware-in-the-loop platform is set up to verify the performance
of the fusion algorithm. The validation results of different aged LIBs over a wide range of temperature show that the presented fusion algorithm can realize a high-accuracy estimation of SOC and capacity with the relative errors within 2% and 3.3%, respectively.

Keywords： State of charge     Capacity estimation     Model fusion     Proportional–integral–differential observer     Hardware-in-the-loop    

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Multi-domain Knowledge Convergence Trajectory Analysis of Strategic Emerging Industries Based on Citation Network and Text Information

Liu Yufei, Miao Zhongzhen, Li Lingfeng, Kong Dejing

Strategic Study of CAE 2020, Volume 22, Issue 2,   Pages 120-129 doi: 10.15302/J-SSCAE-2020.02.016

Abstract:

 The analysis of technology convergence process for strategic emerging industries is helpful to deeply understand the generation process and development law of industrial technology, thereby helping master the development trend of the field and promoting the healthy development of the industry. To identify the trajectory and degree of technology convergence of the strategic emerging industries, this study conducts a multi-case study on four fields which present a trend of convergence and attract social attention, namely, high-end equipment manufacturing, new-generation information technology, new medicine, and new energy. This study adopts a knowledge convergence trajectory analysis method based on citation network and text information. It utilizes a graph neural network model and encodes the citation network, title, and abstract of the publications as vectors. Five knowledge convergence trajectories are identified, after analyzing the data of the selected four technical fields. The research results show that information technology and numerical control equipment, biomedicine and solar photovoltaic technology have shown a trend of deep convergence, respectively; and the convergence of the information technology and numerical control equipment is deeper. Numerical control equipment and solar photovoltaic technology, information technology and solar photovoltaic technology have shown a converging trend, respectively; however, the current degree of convergence is still insufficient, due to the late start of convergence. Numerical control equipment and biomedicine have not shown any trend of convergence.

Keywords： emerging industries     knowledge convergence     graph neural networks     citation network     topic model    

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Image Engineering and Its Research Status in China

Zhang Yujin

Strategic Study of CAE 2000, Volume 2, Issue 8,   Pages 91-94

Abstract:

This paper provides a well-regulated explanation of the definition as well as contents of image engineering, a classification of the theories of image engineering and the applications of image technology. In addition, a comprehensive survey on important Chinese publications about image engineering in the past five years is carried out. An analysis and a discussion of the statistics made on the classification results are also presented. This work shows a general and up-to-date picture of the current status, progress trends and application areas of image engineering in China. It also supplies useful information for readers doing research and/or application works in this field, and provides a helpful reference for editors of journals and potential authors of papers.

Keywords： image engineering     publication     survey    

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A survey on multi-sensor fusion based obstacle detection for intelligent ground vehicles in off-road environments Review

Jin-wen Hu, Bo-yin Zheng, Ce Wang, Chun-hui Zhao, Xiao-lei Hou, Quan Pan, Zhao Xu,hujinwen@nwpu.edu.cn,zhengboyin@mail.nwpu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 5,   Pages 649-808 doi: 10.1631/FITEE.1900518

Abstract: With the development of sensor fusion technologies, there has been a lot of research on intelligent ground vehicles, where is one of the key aspects of vehicle driving. is a complicated task, which involves the diversity of obstacles, sensor characteristics, and environmental conditions. While the on-road driver assistance system or autonomous driving system has been well researched, the methods developed for the structured road of city scenes may fail in an because of its uncertainty and diversity. A single type of sensor finds it hard to satisfy the needs of because of the sensing limitations in range, signal features, and working conditions of detection, and this motivates researchers and engineers to develop and system integration methodology. This survey aims at summarizing the main considerations for the onboard multi-sensor configuration of intelligent ground vehicles in the s and providing users with a guideline for selecting sensors based on their performance requirements and application environments. State-of-the-art methods and system prototypes are reviewed and associated to the corresponding heterogeneous sensor configurations. Finally, emerging technologies and challenges are discussed for future study.

Keywords： Multi-sensor fusion     Obstacle detection     Off-road environment     Intelligent vehicle     Unmanned ground vehicle    

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DDUC: an erasure-coded system with decoupled data updating and coding Research Article

Xiang LI, Yibing LI, Chunrui TANG, Yingsong LI,chunruitang@126.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 5,   Pages 742-758 doi: 10.1631/FITEE.2200253

Abstract: To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of prior knowledge on recognition results, researchers have gradually adopted deep learning techniques to replace traditional modulated signal processing techniques. To address the problem of low recognition accuracy of the modulated signal at low signal-to-noise ratios, we have designed a novel network of multi-scale analysis with deep threshold noise elimination to recognize the actually collected modulated signals under a symmetric cross-entropy function of label smoothing. The network consists of a denoising encoder with deep adaptive threshold learning and a decoder with . The two modules are skip-connected to work together to improve the robustness of the overall network. Experimental results show that this method has better recognition accuracy at low signal-to-noise ratios than previous methods. The network demonstrates a flexible self-learning capability for different noise thresholds and the effectiveness of the designed feature fusion module in multi-scale feature acquisition for various modulation types.

Keywords： Signal noise elimination     Deep adaptive threshold learning network     Multi-scale feature fusion     Modulation recognition    

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Anovel spiking neural network of receptive field encoding with groups of neurons decision Article

Yong-qiang MA, Zi-ru WANG, Si-yu YU, Ba-dong CHEN, Nan-ning ZHENG, Peng-ju REN

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1,   Pages 139-150 doi: 10.1631/FITEE.1700714

Abstract: Human information processing depends mainly on billions of neurons which constitute a complex neural network, and the information is transmitted in the form of neural spikes. In this paper, we propose a spiking neural network (SNN), named MD-SNN, with three key features: (1) using receptive field to encode spike trains from images; (2) randomly selecting partial spikes as inputs for each neuron to approach the absolute refractory period of the neuron; (3) using groups of neurons to make decisions. We test MD-SNN on the MNIST data set of handwritten digits, and results demonstrate that: (1) Different sizes of receptive fields influence classification results significantly. (2) Considering the neuronal refractory period in the SNN model, increasing the number of neurons in the learning layer could greatly reduce the training time, effectively reduce the probability of over-fitting, and improve the accuracy by 8.77%. (3) Compared with other SNN methods, MD-SNN achieves a better classification; compared with the convolution neural network, MD-SNN maintains flip and rotation invariance (the accuracy can remain at 90 44% on the test set), and it is more suitable for small sample learning (the accuracy can reach 80 15% for 1000 training samples, which is 7.8 times that of CNN).

Keywords： Tempotron     Receptive field     Difference of Gaussian (DoG)     Flip invariance     Rotation invariance    

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Interactive medical image segmentation with self-adaptive confidence calibration

沈楚云,李文浩,徐琪森,胡斌,金博,蔡海滨,朱凤平,李郁欣,王祥丰

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9,   Pages 1332-1348 doi: 10.1631/FITEE.2200299

Abstract: Interactive medical image segmentation based on human-in-the-loop machine learning is a novel paradigm that draws on human expert knowledge to assist medical image segmentation. However, existing methods often fall into what we call interactive misunderstanding, the essence of which is the dilemma in trading off short- and long-term interaction information. To better use the interaction information at various timescales, we propose an interactive segmentation framework, called interactive MEdical image segmentation with self-adaptive Confidence CAlibration (MECCA), which combines action-based confidence learning and multi-agent reinforcement learning. A novel confidence network is learned by predicting the alignment level of the action with short-term interaction information. A confidence-based reward-shaping mechanism is then proposed to explicitly incorporate confidence in the policy gradient calculation, thus directly correcting the model’s interactive misunderstanding. MECCA also enables user-friendly interactions by reducing the interaction intensity and difficulty via label generation and interaction guidance, respectively. Numerical experiments on different segmentation tasks show that MECCA can significantly improve short- and long-term interaction information utilization efficiency with remarkably fewer labeled samples. The demo video is available at https://bit.ly/mecca-demo-video.

Keywords： Medical image segmentation     Interactive segmentation     Multi-agent reinforcement learning     Confidence learning     Semi-supervised learning    

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Title Author Date Type Operation

Multi-focus image fusion based on fully convolutional networks

Rui Guo, Xuan-jing Shen, Xiao-yu Dong, Xiao-li Zhang,zhangxiaoli@jlu.edu.cn

Journal Article

Multi-focus image fusion based on fractional-order derivative and intuitionistic fuzzy sets

Xue-feng Zhang, Hui Yan, Hao He,zhangxuefeng@mail.neu.edu.cn

Journal Article

Dual-constraint burst image denoising method

Dan ZHANG, Lei ZHAO, Duanqing XU, Dongming LU,cszhd@zju.edu.cn,cszhl@zju.edu.cn,xdq@zju.edu.cn,ldm@zju.edu.cn

Journal Article

An efficient counter-based Wallace-tree multiplier with a hybrid full adder core for image blending

Ayoub SADEGHI, Nabiollah SHIRI, Mahmood RAFIEE, Mahsa TAHGHIGH

Journal Article

Amultimodal dense convolution network for blind image quality assessment

Nandhini CHOCKALINGAM, Brindha MURUGAN

Journal Article

Recent advances in multisensor multitarget tracking using random finite set

Kai Da, Tiancheng Li, Yongfeng Zhu, Hongqi Fan, Qiang Fu,dktm131@163.com,t.c.li@nwpu.edu.cn,zoyofo@163.com,fanhongqi@nudt.edu.cn

Journal Article

Cooperative planning of multi-agent systems based on task-oriented knowledge fusion with graph neural networks

Hanqi DAI, Weining LU, Xianglong LI, Jun YANG, Deshan MENG, Yanze LIU, Bin LIANG

Journal Article

The Application of Two Dimensions Wavelet in Image Edges Detection

Zhang Hongyan,Zhang Dengpan

Journal Article

Co-Estimation of State of Charge and Capacity for Lithium-Ion Batteries with Multi-Stage Model Fusion Method

Rui Xiong, Ju Wang, Weixiang Shen, Jinpeng Tian, Hao Mu

Journal Article

Multi-domain Knowledge Convergence Trajectory Analysis of Strategic Emerging Industries Based on Citation Network and Text Information

Liu Yufei, Miao Zhongzhen, Li Lingfeng, Kong Dejing

Journal Article

Image Engineering and Its Research Status in China

Zhang Yujin

Journal Article

A survey on multi-sensor fusion based obstacle detection for intelligent ground vehicles in off-road environments

Jin-wen Hu, Bo-yin Zheng, Ce Wang, Chun-hui Zhao, Xiao-lei Hou, Quan Pan, Zhao Xu,hujinwen@nwpu.edu.cn,zhengboyin@mail.nwpu.edu.cn

Journal Article

DDUC: an erasure-coded system with decoupled data updating and coding

Xiang LI, Yibing LI, Chunrui TANG, Yingsong LI,chunruitang@126.com

Journal Article

Anovel spiking neural network of receptive field encoding with groups of neurons decision

Yong-qiang MA, Zi-ru WANG, Si-yu YU, Ba-dong CHEN, Nan-ning ZHENG, Peng-ju REN

Journal Article

Interactive medical image segmentation with self-adaptive confidence calibration

沈楚云,李文浩,徐琪森,胡斌,金博,蔡海滨,朱凤平,李郁欣,王祥丰

Journal Article