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Article ; Online: BiTCAN: A emotion recognition network based on saliency in brain cognition.

An, Yanling / Hu, Shaohai / Liu, Shuaiqi / Li, Bing

Mathematical biosciences and engineering : MBE

2023 Volume 20, Issue 12, Page(s) 21537–21562

Abstract: In recent years, with the continuous development of artificial intelligence and brain-computer interfaces, emotion recognition based on electroencephalogram (EEG) signals has become a prosperous research direction. Due to saliency in brain cognition, we ... ...

Abstract	In recent years, with the continuous development of artificial intelligence and brain-computer interfaces, emotion recognition based on electroencephalogram (EEG) signals has become a prosperous research direction. Due to saliency in brain cognition, we construct a new spatio-temporal convolutional attention network for emotion recognition named BiTCAN. First, in the proposed method, the original EEG signals are de-baselined, and the two-dimensional mapping matrix sequence of EEG signals is constructed by combining the electrode position. Second, on the basis of the two-dimensional mapping matrix sequence, the features of saliency in brain cognition are extracted by using the Bi-hemisphere discrepancy module, and the spatio-temporal features of EEG signals are captured by using the 3-D convolution module. Finally, the saliency features and spatio-temporal features are fused into the attention module to further obtain the internal spatial relationships between brain regions, and which are input into the classifier for emotion recognition. Many experiments on DEAP and SEED (two public datasets) show that the accuracies of the proposed algorithm on both are higher than 97%, which is superior to most existing emotion recognition algorithms.
MeSH term(s)	Artificial Intelligence ; Brain ; Cognition ; Emotions ; Algorithms ; Electroencephalography
Language	English
Publishing date	2023-11-14
Publishing country	United States
Document type	Journal Article
ZDB-ID	2265126-3
ISSN	1551-0018 ; 1551-0018
ISSN (online)	1551-0018
ISSN	1551-0018
DOI	10.3934/mbe.2023953
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Adaptive convolutional sparsity with sub-band correlation in the NSCT domain for MRI image fusion.

Hu, Qiu / Cai, Weiming / Xu, Shuwen / Hu, Shaohai / Wang, Lang / He, Xinyi

Physics in medicine and biology

2024 Volume 69, Issue 5

Abstract: Objective. ...

Abstract	Objective.
MeSH term(s)	Algorithms ; Magnetic Resonance Imaging/methods ; Technology ; Image Processing, Computer-Assisted/methods
Language	English
Publishing date	2024-02-26
Publishing country	England
Document type	Journal Article
ZDB-ID	208857-5
ISSN	1361-6560 ; 0031-9155
ISSN (online)	1361-6560
ISSN	0031-9155
DOI	10.1088/1361-6560/ad2636
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Multi-Focus Image Fusion Based on Multi-Scale Generative Adversarial Network.

Ma, Xiaole / Wang, Zhihai / Hu, Shaohai / Kan, Shichao

Entropy (Basel, Switzerland)

2022 Volume 24, Issue 5

Abstract: The methods based on the convolutional neural network have demonstrated its powerful information integration ability in image fusion. However, most of the existing methods based on neural networks are only applied to a part of the fusion process. In this ...

Abstract	The methods based on the convolutional neural network have demonstrated its powerful information integration ability in image fusion. However, most of the existing methods based on neural networks are only applied to a part of the fusion process. In this paper, an end-to-end multi-focus image fusion method based on a multi-scale generative adversarial network (MsGAN) is proposed that makes full use of image features by a combination of multi-scale decomposition with a convolutional neural network. Extensive qualitative and quantitative experiments on the synthetic and Lytro datasets demonstrated the effectiveness and superiority of the proposed MsGAN compared to the state-of-the-art multi-focus image fusion methods.
Language	English
Publishing date	2022-04-21
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e24050582
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: A Collaborative Despeckling Method for SAR Images Based on Texture Classification

Wang, Gongtang / Bo, Fuyu / Chen, Xue / Lu, Wenfeng / Hu, Shaohai / Fang, Jing

Remote Sensing. 2022 Mar. 18, v. 14, no. 6

2022

Abstract: Speckle is an unavoidable noise-like phenomenon in Synthetic Aperture Radar (SAR) imaging. In order to remove speckle, many despeckling methods have been proposed during the past three decades, including spatial-based methods, transform domain-based ... ...

Abstract	Speckle is an unavoidable noise-like phenomenon in Synthetic Aperture Radar (SAR) imaging. In order to remove speckle, many despeckling methods have been proposed during the past three decades, including spatial-based methods, transform domain-based methods, and non-local filtering methods. However, SAR images usually contain many different types of regions, including homogeneous and heterogeneous regions. Some filters could despeckle effectively in homogeneous regions but could not preserve structures in heterogeneous regions. Some filters preserve structures well but do not suppress speckle effectively. Following this theory, we design a combination of two state-of-the-art despeckling tools that can overcome their respective shortcomings. In order to select the best filter output for each area in the image, the clustering and Gray Level Co-Occurrence Matrices (GLCM) are used for image classification and weighting, respectively. Clustering and GLCM use the co-registered optical images of SAR images because their structure information is consistent, and the optical images are much cleaner than SAR images. The experimental results on synthetic and real-world SAR images show that our proposed method can provide a better objective performance index under a strong noise level. Subjective visual inspection demonstrates that the proposed method has great potential in preserving structural details and suppressing speckle noise.
Keywords	image analysis ; synthetic aperture radar ; texture
Language	English
Dates of publication	2022-0318
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs14061465
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: An Attention-Based Wavelet Convolution Neural Network for Epilepsy EEG Classification.

Xin, Qi / Hu, Shaohai / Liu, Shuaiqi / Zhao, Ling / Zhang, Yu-Dong

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

2022 Volume 30, Page(s) 957–966

Abstract: As a kind of non-invasive, low-cost, and readily available brain examination, EEG has attached significance to the means of clinical diagnosis of epilepsy. However, the reading of long-term EEG records has brought a heavy burden to neurologists and ... ...

Abstract	As a kind of non-invasive, low-cost, and readily available brain examination, EEG has attached significance to the means of clinical diagnosis of epilepsy. However, the reading of long-term EEG records has brought a heavy burden to neurologists and experts. Therefore, automatic EEG classification for epileptic patients plays an essential role in epilepsy diagnosis and treatment. This paper proposes an Attention Mechanism-based Wavelet Convolution Neural Network for epilepsy EEG classification. Attention Mechanism-based Wavelet Convolution Neural Network firstly uses multi-scale wavelet analysis to decompose the input EEGs to obtain their components in different frequency bands. Then, these decomposed multi-scale EEGs are input into the Convolution Neural Network with an attention mechanism for further feature extraction and classification. The proposed algorithm achieves 98.89% triple classification accuracy on the Bonn EEG database and 99.70% binary classification accuracy on the Bern-Barcelona EEG database. Our experiments prove that the proposed algorithm achieves a state-of-the-art classification effect on epilepsy EEG.
MeSH term(s)	Algorithms ; Electroencephalography ; Epilepsy/diagnosis ; Humans ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Wavelet Analysis
Language	English
Publishing date	2022-04-19
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1166307-8
ISSN	1558-0210 ; 1063-6528 ; 1534-4320
ISSN (online)	1558-0210
ISSN	1063-6528 ; 1534-4320
DOI	10.1109/TNSRE.2022.3166181
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder.

An, Yanling / Hu, Shaohai / Duan, Xiaoying / Zhao, Ling / Xie, Caiyun / Zhao, Yingying

Frontiers in computational neuroscience

2021 Volume 15, Page(s) 743426

Abstract: As one of the key technologies of emotion computing, emotion recognition has received great attention. Electroencephalogram (EEG) signals are spontaneous and difficult to camouflage, so they are used for emotion recognition in academic and industrial ... ...

Abstract	As one of the key technologies of emotion computing, emotion recognition has received great attention. Electroencephalogram (EEG) signals are spontaneous and difficult to camouflage, so they are used for emotion recognition in academic and industrial circles. In order to overcome the disadvantage that traditional machine learning based emotion recognition technology relies too much on a manual feature extraction, we propose an EEG emotion recognition algorithm based on 3D feature fusion and convolutional autoencoder (CAE). First, the differential entropy (DE) features of different frequency bands of EEG signals are fused to construct the 3D features of EEG signals, which retain the spatial information between channels. Then, the constructed 3D features are input into the CAE constructed in this paper for emotion recognition. In this paper, many experiments are carried out on the open DEAP dataset, and the recognition accuracy of valence and arousal dimensions are 89.49 and 90.76%, respectively. Therefore, the proposed method is suitable for emotion recognition tasks.
Language	English
Publishing date	2021-10-18
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2452964-3
ISSN	1662-5188
ISSN	1662-5188
DOI	10.3389/fncom.2021.743426
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A Noisy SAR Image Fusion Method Based on NLM and GAN.

Fang, Jing / Ma, Xiaole / Wang, Jingjing / Qin, Kai / Hu, Shaohai / Zhao, Yuefeng

Entropy (Basel, Switzerland)

2021 Volume 23, Issue 4

Abstract: The unavoidable noise often present in synthetic aperture radar (SAR) images, such as speckle noise, negatively impacts the subsequent processing of SAR images. Further, it is not easy to find an appropriate application for SAR images, given that the ... ...

Abstract	The unavoidable noise often present in synthetic aperture radar (SAR) images, such as speckle noise, negatively impacts the subsequent processing of SAR images. Further, it is not easy to find an appropriate application for SAR images, given that the human visual system is sensitive to color and SAR images are gray. As a result, a noisy SAR image fusion method based on nonlocal matching and generative adversarial networks is presented in this paper. A nonlocal matching method is applied to processing source images into similar block groups in the pre-processing step. Then, adversarial networks are employed to generate a final noise-free fused SAR image block, where the generator aims to generate a noise-free SAR image block with color information, and the discriminator tries to increase the spatial resolution of the generated image block. This step ensures that the fused image block contains high resolution and color information at the same time. Finally, a fused image can be obtained by aggregating all the image blocks. By extensive comparative experiments on the SEN1-2 datasets and source images, it can be found that the proposed method not only has better fusion results but is also robust to image noise, indicating the superiority of the proposed noisy SAR image fusion method over the state-of-the-art methods.
Language	English
Publishing date	2021-03-30
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e23040410
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A Boosting SAR Image Despeckling Method Based on Non-Local Weighted Group Low-Rank Representation.

Fang, Jing / Hu, Shaohai / Ma, Xiaole

Sensors (Basel, Switzerland)

2018 Volume 18, Issue 10

Abstract: In this paper, we propose a boosting synthetic aperture radar (SAR) image despeckling method based on non-local weighted group low-rank representation (WGLRR). The spatial structure information of SAR images leads to the similarity of the patches. ... ...

Abstract	In this paper, we propose a boosting synthetic aperture radar (SAR) image despeckling method based on non-local weighted group low-rank representation (WGLRR). The spatial structure information of SAR images leads to the similarity of the patches. Furthermore, the data matrix grouped by the similar patches within the noise-free SAR image is often low-rank. Based on this, we use low-rank representation (LRR) to recover the noise-free group data matrix. To maintain the fidelity of the recovered image, we integrate the corrupted probability of each pixel into the group LRR model as a weight to constrain the fidelity of recovered noise-free patches. Each single patch might belong to several groups, so different estimations of each patch are aggregated with a weighted averaging procedure. The residual image contains signal leftovers due to the imperfect denoising, so we strengthen the signal by leveraging on the availability of the denoised image to suppress noise further. Experimental results on simulated and actual SAR images show the superior performance of the proposed method in terms of objective indicators and of perceived image quality.
Language	English
Publishing date	2018-10-13
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s18103448
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Multimodal Medical Image Fusion using Rolling Guidance Filter with CNN and Nuclear Norm Minimization.

Liu, Shuaiqi / Yin, Lu / Miao, Siyu / Ma, Jian / Cong, Shuai / Hu, Shaohai

Current medical imaging

2020 Volume 16, Issue 10, Page(s) 1243–1258

Abstract: Background: Medical image fusion is very important for the diagnosis and treatment of diseases. In recent years, there have been a number of different multi-modal medical image fusion algorithms that can provide delicate contexts for disease diagnosis ... ...

Abstract	Background: Medical image fusion is very important for the diagnosis and treatment of diseases. In recent years, there have been a number of different multi-modal medical image fusion algorithms that can provide delicate contexts for disease diagnosis more clearly and more conveniently. Recently, nuclear norm minimization and deep learning have been used effectively in image processing. Methods: A multi-modality medical image fusion method using a rolling guidance filter (RGF) with a convolutional neural network (CNN) based feature mapping and nuclear norm minimization (NNM) is proposed. At first, we decompose medical images to base layer components and detail layer components by using RGF. In the next step, we get the basic fused image through the pretrained CNN model. The CNN model with pre-training is used to obtain the significant characteristics of the base layer components. And we can compute the activity level measurement from the regional energy of CNN-based fusion maps. Then, a detail fused image is gained by NNM. That is, we use NNM to fuse the detail layer components. At last, the basic and detail fused images are integrated into the fused result. Results: From the comparison with the most advanced fusion algorithms, the results of experiments indicate that this fusion algorithm has the best effect in visual evaluation and objective standard. Conclusion: The fusion algorithm using RGF and CNN-based feature mapping, combined with NNM, can improve fusion effects and suppress artifacts and blocking effects in the fused results.
MeSH term(s)	Algorithms ; Artifacts ; Cell Nucleus ; Image Processing, Computer-Assisted ; Neural Networks, Computer
Language	English
Publishing date	2020-08-14
Publishing country	United Arab Emirates
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1573-4056
ISSN (online)	1573-4056
DOI	10.2174/1573405616999200817103920
Database	MEDical Literature Analysis and Retrieval System OnLINE

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