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SAR marine oil spill detection based on an encoder-decoder network

INTERNATIONAL JOURNAL OF REMOTE SENSING 2024年第2期45卷 587-608页

作者： Soh, Kexin Zhao, Lingli Peng, Min Lu, Jianzhong Sun, Weidong Tongngern, Suchada Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Shenyang Geotech Invest & Surveying Res Inst Co Lt Shenyang Peoples R China

Marine oil spills are currently among the most challenging problems in marine environments. Synthetic Aperture Radar (SAR), with all-day and all-weather observation, has demonstrated great potential in oil disaster monitoring. A simple encoder-decoder network model was proposed by using SAR images for effective oil spill detection. The proposed model incorporates an optimized U-Net architecture that reduces computational requirements while maintaining detection performance. This is achieved through shortened encoder and decoder stages, depthwise separable convolutions, group normalization, and bilinear interpolation-based upsampling. To improve the model's generalization, the auto-learning rate and focal loss function are also included. Two public SAR datasets acquired by different sensors have been used to illustrate the efficiency of the proposed model. In addition, the polarimetric information has also been assessed for the detection of oil spill monitoring. The results show that the model can achieve high efficiency at a small model size. The sub-dataset with polarimetric features also achieves a high F1-score of 91.65% and an IoU of 84.59%.

关键词： SAR oil spill detection encoder-decoder network U-Net model polarization

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Internal and external transmission encoder-decoder network for single-image deraining

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VISUAL COMPUTER 2024年第12期40卷 8653-8663页

作者： Xu, Yingcheng Han, Congwei Lv, Shuqi Wang, Ze Wang, Miao Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan 250014 Peoples R China

The visual quality of images captured in rainy weather is degraded by rain streaks, which may significantly reduce the accuracy of computer vision systems. In this paper, an internal and external transmission encoder-decoder network is proposed for eliminating rain streaks in a single image. Since rain streaks and background structures tend to be spatially long, we apply an internal connected aggregation unit to leverage global information and aggregate features at different scales to help remove rain streaks while restoring scene details. To fully exploit effective features of the encoder network and remove redundant information, we utilize an external connected enhancement unit to obtain effective feature maps for predicting clear outputs. By utilizing the efficient information inside and outside the unit, the proposed method can remove the rain streaks of different sizes and directions in the image and restore clear background structure details. Extensive experimental results on synthetic datasets and natural samples show that our method can achieve better rain removal performance than other advanced methods.

关键词： Internal and external transmission encoder-decoder network Image deraining

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Efficient-Unet: Intelligent identification of abrasive grain on the entire surface of monolayer brazing wheel based on encoder-decoder network

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第12期131卷 6027-6037页

作者： Chen, Junying Wang, Boxuan Lin, Yiming Chen, Xiuyu Jiang, Qingshan Cui, Changcai Jimei Univ Coll Marine Equipment & Mech Engn Xiamen 361021 Peoples R China Huaqiao Univ Inst Mfg Engn Xiamen 361021 Peoples R China

Measuring and extracting abrasive grains on the entire surface of monolayer brazing grinding wheels to analyze the distribution of abrasive grains are of great significance to grinding research and grinding wheel manufacture. It is not easy to carry out the work with traditional methods. In this paper, a linear CCD is used to acquire the entire grinding wheel surface image, and an improved encoder-decoder network based on Efficientnet, ASPP, and Skip Connections is designed to promote the accuracy and speed of abrasive grains prediction. Based on dataset creation, transfer learning, and hyper-parameter testing, 91.21% abrasive grain semantic segmentation accuracy was finally obtained. If we focus on whether the abrasive grains are recognized without considering semantic errors, an accuracy rate of 99.6% is obtained. The method can provide basic data for grinding mechanism research and abrasive tool manufacturing.

关键词： Grinding wheel abrasive grain Intelligent identification encoder-decoder network ASPP

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Robust Boundary Segmentation in Medical Images Using a Consecutive Deep encoder-decoder network

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IEEE ACCESS 2019年 7卷 33795-33808页

作者： Ngoc-Quang Nguyen Lee, Sang-Woong Gachon Univ Pattern Recognit & Machine Learning Lab Seongnam 13120 South Korea

Image segmentation is typically used to locate objects and boundaries. It is essential in many clinical applications, such as the pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment. The segmentation task is hampered by fuzzy boundaries, complex backgrounds, and appearances of objects of interest, which vary considerably. The success of the procedure is still highly dependent on the operator's skills and the level of hand-eye coordination. Thus, this paper was strongly motivated by the necessity to obtain an early and accurate diagnosis of a detected object in medical images. In this paper, we propose a new polyp segmentation method based on the architecture of a multiple deep encoder-decoder networks combination called CDED-net. The architecture can not only hold multi-level contextual information by extracting discriminative features at different effective fields-of-view and multiple image scales but also learn rich information features from missing pixels in the training phase. Moreover, the network is also able to capture object boundaries by using multiscale effective decoders. We also propose a novel strategy for improving the method's segmentation performance based on a combination of a boundary-emphasization data augmentation method and a new effective dice loss function. The goal of this strategy is to make our deep learning network available with poorly defined object boundaries, which are caused by the non-specular transition zone between the background and foreground regions. To provide a general view of the proposed method, our network was trained and evaluated on three well-known polyp datasets, CVC-ColonDB, CVC-ClinicDB, and ETIS-Larib PolypDB. Furthermore, we also used the Pedro Hispano Hospital (PH2), ISBI 2016 skin lesion segmentation dataset, and CT healthy abdominal organ segmentation dataset to depict our network's ability. Our results reveal that the CDED-net significantly surpasses the state-of-the-art m

关键词： Image segmentation medical image segmentation encoder-decoder network boundary segmentation continuous network deep convolutional neural network

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Private Cell-ID Trajectory Prediction Using Multi-Graph Embedding and encoder-decoder network

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2022年第8期21卷 2967-2977页

作者： Lv, Mingqi Zeng, Dajian Chen, Ling Chen, Tieming Zhu, Tiantian Ji, Shouling Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China

Trajectory prediction for mobile phone users is a cornerstone component to support many higher-level applications in LBSs (Location-Based Services). Most existing methods are designed based on the assumption that the explicit location information of the trajectories is available (e.g., GPS trajectories). However, collecting such kind of trajectories lays a heavy burden on the mobile phones and incurs privacy concerns. In this paper, we study the problem of trajectory prediction based on cell-id trajectories without explicit location information and propose a deep learning framework (called DeepCTP) to solve this problem. Specifically, we use a multi-graph embedding method to learn the latent spatial correlations between cell towers by exploiting handoff patterns. Then, we design a novel spatial-aware loss function for the encoder-decoder network to generate cell-id trajectory predictions. We conducted extensive experiments on real datasets. The experiment results show that DeepCTP outperforms the state-of-the-art cell-id trajectory prediction methods in terms of prediction error.

关键词： Trajectory Mobile handsets Predictive models Correlation Poles and towers Hidden Markov models Mathematical model Trajectory prediction cell-id trajectory graph embedding encoder-decoder network mobile phone

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ResMIBCU-Net: an encoder-decoder network with residual blocks, modified inverted residual block, and bi-directional ConvLSTM for impacted tooth segmentation in panoramic X-ray images

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ORAL RADIOLOGY 2023年第4期39卷 614-628页

作者： Imak, Andac Celebi, Adalet Polat, Onur Turkoglu, Muammer Sengur, Abdulkadir Munzur Univ Fac Engn Dept Elect & Elect Engn Tunceli Turkiye Mersin Univ Fac Dent Oral & Maxillofacial Surg Dept Mersin Turkiye Gazi Univ Fac Technol Dept Comp Engn Ankara Turkiye Samsun Univ Fac Engn Dept Software Engn Samsun Turkiye Firat Univ Fac Technol Dept Elect & Elect Engn Elazig Turkiye

ObjectiveImpacted tooth is a common problem that can occur at any age, causing tooth decay, root resorption, and pain in the later stages. In recent years, major advances have been made in medical imaging segmentation using deep convolutional neural network-based networks. In this study, we report on the development of an artificial intelligence system for the automatic identification of impacted tooth from panoramic dental X-ray *** existing networks, in medical imaging segmentation, U-Net architectures are widely implemented. In this article, for dental X-ray image segmentation, blocks and convolutional block structures using inverted residual blocks are upgraded by taking advantage of U-Net's network capacity-intensive connections. At the same time, we propose a method for jumping connections in which bi-directional convolution long short-term memory is used instead of a simple connection. Assessment of the proposed artificial intelligence model performance was evaluated with accuracy, F1-score, intersection over union, and *** the proposed method, experimental results are obtained with 99.82% accuracy, 91.59% F1-score, 84.48% intersection over union, and 90.71% *** findings show that our artificial intelligence system could help with future diagnostic support in clinical practice.

关键词： Impacted tooth detection encoder-decoder network Deep learning Oral health Panoramic radiography

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Handwriting Trajectory Reconstruction Using Spatial-Temporal encoder-decoder network 4th

Handwriting Trajectory Reconstruction Using Spatial-Temporal...

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4th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Wei, Feilong Zhu, Yuanping Tianjin Normal Univ 393 Binshuixi Rd Tianjin Peoples R China

ISBN: (纸本)9783030880040;9783030880033

Chinese handwriting characters have complex strokes and various writing styles, which makes it difficult to reconstruct handwriting. Aiming at this problem, we propose a handwriting reconstruction method based on a spatial-temporal encoder-decoder network with constrains. Different from other models that generate trajectory coordinates through a fully connected network, the method proposed in this paper outputs heat map sequence. The model is consists of three modules: key point detection module, spatial encoder-decoder module and reconstruction constraint module. The key point detector module and the spatial encoder part of encoder-decoder module are composed of a full convolutional network. The former generates heat maps of all key points which is a branch of the spatial encoder, and the mainly encoding the spatial information of each position on the offline image. The temporal decoder module is composed of a GRU network and an MLP network. Finally, we combine temporal information and reconstruction constraints to generate the final sequence. At each time, the features encoding by the spatial encoder module are combined with the features at the previous time that generate a corresponding heat map. The main contribution of the work of this paper is to propose a method that more suitable for handwriting reconstruction of Chinese handwritten characters. Experimental results show that the CT [6] accuracy of our method has already reached 87.6% on OLHWDB1.1 dataset.

关键词： Handwriting trajectory reconstruction Full convolutional network encoder-decoder network Deep learning.

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Free-Form Image Inpainting with Separable Gate encoder-decoder network 28th

Free-Form Image Inpainting with Separable Gate Encoder-Decod...

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28th International Conference on Neural Information Processing

作者： Nie, Liang Yu, Wenxin Zhang, Xuewen Li, Siyuan Chen, Shiyu Zhang, Zhiqiang Gong, Jun Southwest Univ Sci & Technol Mianyang Sichuan Peoples R China Hosei Univ Tokyo Japan Beijing Inst Technol Beijing Peoples R China

ISBN: (纸本)9783030922375;9783030922382

Image inpainting refers to the process of reconstructing damaged areas of an image. For image inpainting, there are many means to generate not too bad inpainting results today. However, these methods either make the results look unrealistic or have complex structures and a large number of parameters. In order to solve the above problems, this paper designed a simple encoder-decoder network and introduced the region normalization technique. At the same time, a new separable gate convolution is proposed. The simple network architecture and separable gate convolution significantly reduce the number of network parameters. Moreover, the separable gate convolution can learn the mask (represents the missing area) from the feature map and update it automatically. After mask update, weights will be applied to each pixel of the feature map to alleviate the impact of invalid mask information on the completed result and improve the inpainting quality. Our method reduces 0.58M parameters. Moreover, our method improved the PSNR of Celeba and Paris Street View by 0.7-1.4 dB and 0.7-1.0 dB, respectively, in 10% to 60% damage cases. The corresponding SSIM has been increased 1.6 to 2.7 and 0.9 to 2.3%.

关键词： Image inpainting Separable gate convolution encoder-decoder network

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VISIBLE AND INFRARED IMAGE FUSION USING encoder-decoder network

VISIBLE AND INFRARED IMAGE FUSION USING ENCODER-DECODER NETW...

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IEEE International Conference on Image Processing (ICIP)

作者： Ataman, Ferhat Can Bozdagi Akar, Gozde Middle East Tech Univ Ankara Turkey

ISBN: (纸本)9781665441155

The aim of multispectral image fusion is to combine object or scene features of images with different spectral characteristics to increase the perceptual quality. In this paper, we present a novel learning-based solution to image fusion problem focusing on infrared and visible spectrum images. The proposed solution utilizes only convolution and pooling layers together with a loss function using no-reference quality metrics. The analysis is performed qualitatively and quantitatively on various datasets. The results show better performance than stateof-the-art methods. Also, the size of our network enables real-time performance on embedded devices. Project codes can be found at https://***/ferhatcan/ pyFusionSR.

关键词： infrared visible images image fusion deep learning encoder-decoder network

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Image Inpainting Forensics Algorithm Based on Dual-Domain encoder-decoder network 1

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23rd International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Zhang, Dengyong Tan, En Li, Feng Liu, Shuai Wang, Jing Hu, Jinbin Changsha Univ Sci & Technol Sch Comp & Commun Engn Hunan Prov Key Lab Intelligent Proc Big Data Tran Changsha 410004 Peoples R China Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410004 Peoples R China

ISBN: (数字)9789819708086

ISBN: (纸本)9789819708079;9789819708086

Image inpainting can fill regions of images with plausible content and can also be used to remove specific objects and leave only faint traces in inpainted images, which pose serious security issues. At present, there are relatively few forensic works on image inpainting. Moreover, there is a problem of poor generalization. Therefore, This paper proposes a dual-domain encoder-decoder network (DDEDNet) based on different input types, which is a two-branch network. The first branch is a spatial domain-based encoder network (S-encoder) used to capture the tampering traces left by image inpainting in the spatial domain;the second branch is an encoder network based on the frequency domain (F-encoder), which is used to mine the subtle artifacts left in the frequency domain. Then a cross-modal attention fusion module (CMAF) is used to fuse the features of the two encoder networks to obtain rich fused features. Finally, attention-gated (AG) skip connections are utilized to improve localization performance by properly incorporating multi-scale features in the decoder. Experimental results show that in the face of data sets with both deep inpainting and traditional schemes, DDEDNet can locate the inpainting area more accurately, effectively resist JPEG compression and Gaussian noise attacks, and performs better generalization.

关键词： Image inpainting forensics encoder-decoder network Frequency domain Cross-modal attention Attention gate

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