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Context-Enhanced Representation Learning for Single Image Deraining

INTERNATIONAL JOURNAL OF COMPUTER VISION 2021年第5期129卷 1650-1674页

作者： Wang, Guoqing Sun, Changming Sowmya, Arcot CSIRO Data61 POB 76 Epping NSW 1710 Australia Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia

Perception of content and structure in images with rainstreaks or raindrops is challenging, and it often calls for robust deraining algorithms to remove the diversified rainy effects. Much progress has been made on the design of advanced encoder-decoder single image deraining networks. However, most of the existing networks are built in a blind manner and often produce over/under-deraining artefacts. In this paper, we point out, for the first time, that the unsatisfactory results are caused by the highly imbalanced distribution between rainy effects and varied background scenes. Ignoring this phenomenon results in the representation learned by the encoder being biased towards rainy regions, while paying less attention to the valuable contextual regions. To resolve this, a context-enhanced representation learning and deraining network is proposed with a novel two-branch encoder design. Specifically, one branch takes the rainy image directly as input for learning a mixed representation depicting the variation of both rainy regions and contextual regions, and another branch is guided by a carefully learned soft attention mask to learn an embedding only depicting the contextual regions. By combining the embeddings from these two branches with a carefully designed co-occurrence modelling module, and then improving the semantic property of the co-occurrence features via a bi-directional attention layer, the underlying imbalanced learning problem is resolved. Extensive experiments are carried out for removing rainstreaks and raindrops from both synthetic and real rainy images, and the proposed model is demonstrated to produce significantly better results than state-of-the-art models. In addition, comprehensive ablation studies are also performed to analyze the contributions of different designs. Code and pre-trained models will be publicly available at .

关键词： Image deraining encoder-decoder network Imbalanced distribution Context-enhanced representation learning

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A novel data-temporal attention network based strategy for fault diagnosis of chiller sensors

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ENERGY AND BUILDINGS 2019年第Sep.期198卷 377-394页

作者： Li, Ding Li, Donghui Li, Chengdong Li, Lin Gao, Long Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Shandong Peoples R China

In the air-cooled chiller system, sensor fault diagnosis has great significance for ensuring normal operation. However, according to the operating mechanism of the chiller system, sensors' readings exhibit dynamical data-temporal dependencies and are easily affected by external factors and control parameters. To further capture the data characteristics existed in the sensors time series, in this paper, we propose a novel data-temporal attention network (DAN) for the chiller sensor fault diagnosis. Based on the conventional encoder-decoder network (EDN), the proposed DAN model is firstly built by adding three novel parts: the first one is a data attention mechanism embedded in the encoder, which is used to capture the dynamic data correlation between different sensors;the second part is a temporal attention mechanism, which is employed in the decoder to model the dynamic time-dependencies among the sensors time series;considering the influence of external factors and control parameters, the third part is a fusion module to incorporate these influential factors from different domains. Thereafter, we design a specific chiller sensor fault diagnosis strategy using the proposed DAN model. The sensor fault diagnosis strategy uses only normal sequences for training and learns to reconstruct normal time series behaviors, and then determines the fault threshold of each chiller sensor, and finally identifies the specific sensor fault by comparing the absolute reconstruction error vector with the fault threshold vector. In the end, the experiments which adopt data sets from a real air-cooled chiller platform are conducted, and detailed comparisons are made. Various magnitudes of fixed biases are introduced into eleven sensors for validation. Experimental results reveal that the sensor fault diagnosis strategy with the proposed DAN model achieves the best training and fault diagnosis performance compared with its variants and the traditional EDN model. Especially for the sensor

关键词： Air-cooled chiller Sensor fault diagnosis encoder-decoder network Attention mechanism Deep learning Fixed biases

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EMYNet-BDD: EfficientViTB Meets Yolov8 in the encoder-decoder Architecture for Building Damage Detection Using Postevent Remote Sensing Images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年第1期17卷 13120-13134页

作者： Gomroki, Masoomeh Hasanlou, Mahdi Chanussot, Jocelyn Hong, Danfeng Univ Tehran Sch Surveying & Geospatial Engn Coll Engn Tehran *** Iran Univ Grenoble Alpes GIPSA Lab CNRS Grenoble INP F-38000 Grenoble France Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

Natural disasters commonly occur in all regions around the world and cause huge financial and human losses. One of the main effects of earthquakes and floods is the destruction of buildings. Photogrammetric and remote sensing (RS) data track changes and detect damages in these events. Considering the evolution in deep learning (DL) techniques, the possibility of accurate information extraction from the RS-based data is increased. DL methods effectively show the damaged regions for decision making and immediate actions for crisis management. The present study is based only on postevent RS images, which apply an encoder-decoder network composed of pretrained EfficientViTB and Yolov8 network blocks as encoder path and the modified-Unet blocks as decoder path for building damage detection (BDD). Compared with methods that use only one network in their encoder path, the presented method achieves better results. To investigate the performance of the proposed method, three datasets affected by different natural disasters are considered. The first dataset is the satellite images of the 2023 Turkey earthquake, the second dataset is associated with the satellite images of the 2023 Morocco earthquake, and the third dataset contains the satellite images of the 2023 Libya flood. The proposed method ultimately reaches the overall accuracy of 97.62%, 98.63%, and 96.43% and the kappa coefficient of 0.86, 0.85, and 0.84 for the first, second, and third dataset, respectively, which shows the proper performance of the proposed method for BDD.

关键词： Building damage detection (BDD) EfficientViTB encoder-decoder network Libya flood Morocco earthquake Turkey earthquake Yolov8

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IRU-Net: An Efficient End-to-End network for Automatic Building Extraction From Remote Sensing Images

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IEEE ACCESS 2022年 10卷 37811-37828页

作者： Sheikh, Md Abdul Alim Maity, Tanmoy Kole, Alok Aliah Univ Dept Elect & Commun Engn Kolkata 700160 India Indian Inst Technol ISM Dept Min Machinery Engn Dhanbad 826004 Bihar India RCC Inst Informat Technol Dept Elect Engn Kolkata 700015 India

Automatic extraction of buildings from High-Resolution Remote Sensing (RS) Imagery is of great practical interest for numerous applications;including urban planning, change detection, disaster management, estimation of human population, and many other geospatial related applications. This paper proposes a novel efficient Improved ResU-Net architecture called IRU-Net, integrating spatial pyramid pooling module with an encoder-decoder structure, in combination with Atrous convolutions, modified residual connections, and a new skip connection between the encoder-decoder features for automatic extraction of buildings from RS images. Moreover, a new dual loss function called binary cross-entropy-dice-loss (BCEDL) is opted that make cross-entropy (CE) and dice loss (DL) and consider both local information and global information to decrease the class imbalance influence and improve the building extraction results. The proposed model is examined to demonstrate its generalization on two publicly available datasets;the Aerial Images for Roof Segmentation (AIRS) Dataset and the Massachusetts buildings dataset. The proposed IRU-Net achieved an average F-1 accuracy of 92.34% for the Massachusetts dataset and 95.65% for the AIRS dataset. When compared to other state-of-the-art deep learning-based models such as SegNet, U-Net, E-Net, ERFNet and SRI-Net, the overall accuracy improvements of our IRU-Net model is 9.0% (0.9725 vs. 0.8842), 5.2% (0.9725 vs. 0.9218), 3.0% (0.9725 vs. 0.9428), 1.4% (0.9725 vs. 0.9588) and 0.93% (0.9725 vs. 0.9635), for AIRS dataset and 11.6%, 5.9%, 3.1%, 2.7% and 1.4%, for Massachusetts building dataset. These results prove the superiority of the proposed model for building extraction from high-resolution RS images.

关键词： Buildings Feature extraction Image segmentation Architecture Data mining Computational modeling Task analysis Building extraction deep learning encoder-decoder network atrous spatial pyramid pooling remote sensing imagery cross-entropy and dice loss

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ERDUnet: An Efficient Residual Double-Coding Unet for Medical Image Segmentation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第4期34卷 2083-2096页

作者： Li, Hao Zhai, Di-Hua Xia, Yuanqing Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Beijing Inst Technol Yangtze Delta Reg Acad Jiaxing 314001 Peoples R China

Medical image segmentation is widely used in clinical diagnosis, and methods based on convolutional neural networks have been able to achieve high accuracy. However, it is still difficult to extract global context features, and the parameters are too large to be clinically applied. In this regard, we propose a novel network structure to improve the traditional encoder-decoder network model, which saves parameters while maintaining segmentation accuracy. We improve the feature extraction efficiency by constructing an encoder module that can simultaneously extract local features and global continuity information. A novel attention module is designed to optimize segmentation boundary regions while improving training efficiency. The feature transfer structure of the decoding part is also improved, which fully integrates the features of different levels to restore the spatial resolution more finely. We evaluate our model on seven different medical segmentation datasets, the 2018 Data Science Bowl Challenge (DSBC2018), the 2018 Lesion Boundary Segmentation Challenge (ISIC2018), the Gland Segmentation in Colon Histology Images Challenge (GlaS), Kvasir-SEG, CVC-ClinicDB, Kvasir-Instrument and Polypgen. Extensive experimental results show that our model can achieve good segmentation performance while maintaining a small number of parameters and computational load, which can further facilitate the generalization of the theoretical approach to clinical practice. Our code will be released at https://***/caijilia/ERDUnet.

关键词： Medical image segmentation deep learning encoder-decoder network convolutional neural network reduce parameter scale

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Representation learning in a deep network for license plate recognition

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第19-20期79卷 13267-13289页

作者： Rakhshani, Sajed Rashedi, Esmat Nezamabadi-pour, Hossein Grad Univ Adv Technol Dept Elect & Comp Engn POB 76315 Kerman Iran Shahid Bahonar Univ Kerman Dept Elect Engn IDPL Kerman Iran

The goal of license plate recognition (LPR) is to read the license plate characters. Due to image degradation, there are many difficulties in the way of achieving this goal. In this paper, the proposed method recognizes the license plate characters without employing the traditional segmentation and binarization techniques. This method uses a deep learning algorithm and tries to achieve better learning experience by engaging a multi-task learning algorithm based on sharing features. The features of license plate characters are extracted by a deep encoder-decoder network, and transferred to 8 parallel classifiers for recognition. To evaluate the current work, a database of 11,000 license plate images, collected from a currently working surveillance system installed on a dual carriageway, is employed. The proposed method achieved the correct character recognition rate of 96% for 4000 test images that is acceptable in comparison to the competing methods.

关键词： Deep learning Representation learning encoder-decoder network License plate recognition

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Redefining Retinal Lesion Segmentation: A Quantum Leap With DL-UNet Enhanced Auto encoder-decoder for Fundus Image Analysis

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IEEE ACCESS 2023年 11卷 70853-70864页

作者： Kumar, B. Naveen Mahesh, T. R. Geetha, G. Guluwadi, Suresh JAIN Deemed Univ Fac Engn & Technol Dept Comp Sci & Engn Bengaluru 562112 India Adama Sci & Technol Univ Dept Mech Engn Adama Ethiopia

The first diagnosis of diabetic retinopathy (DR) must include lesion segmentation. As it takes a lot of time and effort to label lesions, automatic segmentation methods have to be created manually. The degree of the retina's degenerative lesions determines how severe diabetic retinopathy is. A major influence is on the early detection of illness and treatment of DR. To reliably identify the sites of related lesions and identify various abnormalities in retinal fundus pictures, deep learning algorithms are crucial. Additionally, utilizing patch-based analysis, a deep convolutional neural network is constructed. In this study, encoder-decoder neural networks along with channel-wise spatial Attention Mechanisms are proposed. The IDRiD dataset, which includes hard exudate segmentations, is used to train and evaluate the architecture. In this method, image patches are created using the sliding window technique. To determine the effectiveness of the recommended strategy, a thorough experiment was conducted on IDRiD. In order to predict the various sorts of lesions, the trained network analyses the picture patches and creates a probability map. This technique's efficacy and supremacy are confirmed by the expected accuracy of 99.94 %. The findings of this experiment show significantly enhanced performance in terms of accuracy when compared to prior research on comparable tasks.

关键词： Diabetic retinopathy patch generation fundus images lesion segmentation encoder-decoder network convolutional neural network spatial attention mechanism deep learning

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DESN: An unsupervised MR image denoising network with deep image prior

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THEORETICAL COMPUTER SCIENCE 2021年 880卷 97-110页

作者： Zhu, Yazhou Pan, Xiang Lv, Tianxu Liu, Yuan Li, Lihua Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi Jiangsu Peoples R China Hangzhou Dianzi Univ Coll Life Informat Sci & Instrument Engn Hangzhou Peoples R China

Magnetic Resonance Imaging (MRI) is a widely used medical diagnosis technique. However, the quality of MR image is affected by the noise which is caused by mechanical and environmental reasons during the MR image acquisition process. For decades, various kinds of methods including filtering approaches, transform domain approaches and statistical approaches have been applied to the MR image denoising problem, while there are also some drawbacks exiting in these methods such as arising undesirable change of texture and long computation time needed. In this paper, we proposed a novel MR image denoising method called DESN which is a neural network method and has a novel network architecture with well-designed loss function. In DESN, the convolutional neural networks itself is considered as a regularizer or image prior information for the inverse problems such as denoising. The network architecture of DESN is designed from the auto-encoder architecture, it has three main parts: encoder network for extracting low-resolution image features, decoder network for restoring high-resolution features and skip connections for transmitting abstract information from encoder network to decoder network. Besides, we also design a novel loss function which contains two main parts: data fidelity loss (L-fidelity), image quality penalty (L-q) and three loss terms: mean squared error term (L-MSE), image structure similarity term (L-S), image information entropy term (L-IE). We compare the performance of DESN with DIP and some state of the art denoising methods, and the performance of our network with different loss terms are also compared in three MR modalities. The comparative results show that DESN have the superior performance in generating high-quality MR image with enough edge and texture information. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Deep learning MRI denoising encoder-decoder network Novel loss function

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3D-RVP: A method for 3D object reconstruction from a single depth view using voxel and point

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NEUROCOMPUTING 2021年 430卷 94-103页

作者： Zhao, Meihua Xiong, Gang Zhou, MengChu Shen, Zhen Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent Cloud Comp Ctr Dongguan 523808 Peoples R China New Jersey Inst Technol Helen & John C Hartmann Dept Elect & Comp Engn Newark NJ 07102 USA Qingdao Acad Intelligent Ind Intelligent Mfg Ctr Qingdao 266109 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China

Three-dimensional object reconstruction technology has a wide range of applications such as augment reality, virtual reality, industrial manufacturing and intelligent robotics. Although deep learning-based 3D object reconstruction technology has developed rapidly in recent years, there remain important problems to be solved. One of them is that the resolution of reconstructed 3D models is hard to improve because of the limitation of memory and computational efficiency when deployed on resource-limited devices. In this paper, we propose 3D-RVP to reconstruct a complete and accurate 3D geometry from a single depth view, where R, V and P represent Reconstruction, Voxel and Point, respectively. It is a novel two-stage method that combines a 3D encoder-decoder network with a point prediction network. In the first stage, we propose a 3D encoder-decoder network with residual learning to output coarse prediction results. In the second stage, we propose an iterative subdivision algorithm to predict the labels of adaptively selected points. The proposed method can output high-resolution 3D models by increasing a small number of parameters. Experiments are conducted on widely used benchmarks of a ShapeNet dataset in which four categories of models are selected to test the performance of neural networks. Experimental results show that our proposed method outperforms the state-of-the-arts, and achieves about 2:7% improvement in terms of the intersection-over-union metric. (c) 2020 Elsevier B.V. All rights reserved.

关键词： 3D object reconstruction encoder-decoder network Machine learning Point prediction network

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AEAU-Net: An unsupervised end-to-end registration network by combining affine transformation and deformable medical image registration (Jul, 10.1007/s11517-023-02887-y, 2023)

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2023年第11期61卷 2875-2875页

作者： Qiu, Wei Xiong, Lianjin Li, Ning Luo, Zhangrong Wang, Yaobin Zhang, Yangsong Southwest Univ Sci & Technol Sch Comp Sci & Technol Lab Brain Sci & Med Artificial Intelligence Mianyang 621010 Peoples R China Mianyang Cent Hosp NHC Key Lab Nucl Technol Med Transformat Mianyang 621010 Peoples R China Southwest Univ Sci & Technol Key Lab Testing Technol Mfg Proc Minist Educ Mianyang 621010 Sichuan Peoples R China

Deformable medical image registration plays an essential role in clinical diagnosis and treatment. However, due to the large difference in image deformation, unsupervised convolutional neural network (CNN)-based methods cannot extract global features and local features simultaneously and cannot capture long-distance dependencies to solve the problem of excessive deformation. In this paper, an unsupervised end-to-end registration network is proposed for 3D MRI medical image registration, named AEAU-Net, which includes two-stage operations, i.e., an affine transformation and a deformable registration. These two operations are implemented by an affine transformation subnetwork and a deformable registration subnetwork, respectively. In the deformable registration subnetwork, termed as EAU-Net, we designed an efficient attention mechanism (EAM) module and a recursive residual path (RSP) module. The EAM module is embedded in the bottom layer of the EAU-Net to capture long-distance dependencies. The RSP model is used to obtain effective features by fusing deep and shallow features. Extensive experiments on two datasets, LPBA40 and Mindboggle101, were conducted to verify the effectiveness of the proposed method. Compared with baseline methods, this proposed method could obtain better registration performance. The ablation study further demonstrated the reasonability and validity of the designed architecture of the proposed method.

关键词： Deformable image registration Affine transformation encoder-decoder network Attention mechanism Two-stage registration

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