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Underwater image restoration using deep encoder-decoder network with symmetric skip connections

SIGNAL IMAGE AND VIDEO PROCESSING 2022年第1期16卷 247-255页

作者： Gangisetty, Shankar Rai, Raghu Raj KLE Technol Univ Hubballi India

Underwater images get degraded for a variety of naturally occurring attributes like haze, suspended particles, light scattering and water types. The primary cause of the degradation is underwater light attenuation that varies with wavelength, unlike the uniform attenuation that occurs in-air. In this paper, we propose an end-to-end deep convolutional neural network architecture to restore the underwater images and improve their visual perception. The encoder learns to encode the degraded image to a lower-dimensional feature map, while the decoder learns to restore the image to a degradation-free form. This is achieved due to the utilization of symmetric skip connections between the encoder-decoder blocks for the propagation of feature maps to improve the sharpness of the restored image and prevent the loss of details caused by the convolutions. We exhaustively evaluate the performance of our network both qualitatively and quantitatively on standard datasets, and the effectiveness of our network is demonstrated with existing methods of underwater image restoration and enhancement techniques.

关键词： Underwater image restoration and enhancement Convolutional neural networks Skip connections encoder-decoder UIEB dataset

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encoder-decoder Design for SAC-OCDMA using Flexible Cross Correlation (FCC) Code Algorithms

Encoder-Decoder Design for SAC-OCDMA using Flexible Cross Co...

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IEEE 4th International Conference on Photonics (ICP)

作者： Rashidi, C. B. M. Aljunid, S. A. Fadhil, Hilal A. Anuar, M. S. Univ Malaysia Perlis UniMAP Sch Comp & Commun Eng Kangar 01000 Perlis Malaysia

ISBN: (纸本)9781467360739;9781467360753

In this paper, we present an effective encoder-decoder design utilizing Flexible Cross Correlation (FCC) code for Spectral Amplitude Coding-Optical Code Division Multiple Access (SAC-OCDMA) systems. The FCC code has advantages, such as flexibility cross-correlation property for any given number of users and weights, as well as effectively reduces the impacts of Multiple-Access Interference (MAI). The proposed FCC SAC-OCDMA encoder-decoder had shown superior performance indicated FCC SAC-OCDMA encoder-decoder offers 100%, 287% and 331% much larger number of active users compared to MDW K=60, MFH K=31 and Hadamard K=29, respectively.

关键词： FCC Code encoder-decoder MAI SAC-OCDMA Systems

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encoder-decoder WITH FOCUS-MECHANISM FOR SEQUENCE LABELLING BASED SPOKEN LANGUAGE UNDERSTANDING

ENCODER-DECODER WITH FOCUS-MECHANISM FOR SEQUENCE LABELLING ...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhu, Su Yu, Kai Shanghai Jiao Tong Univ Brain Sci & Technol Res Ctr Key Lab Shanghai Educ Commiss Intelligent Interac SpeechLabDept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781509041176

This paper investigates the framework of encoder-decoder with attention for sequence labelling based spoken language understanding. We introduce Bidirectional Long Short Term Memory - Long Short Term Memory networks (BLSTM-LSTM) as the encoder-decoder model to fully utilize the power of deep learning. In the sequence labelling task, the input and output sequences are aligned word by word, while the attention mechanism cannot provide the exact alignment. To address this limitation, we propose a novel focus mechanism for encoder-decoder framework. Experiments on the standard ATIS dataset showed that BLSTM-LSTM with focus mechanism defined the new state-of-the-art by outperforming standard BLSTM and attention based encoder-decoder. Further experiments also show that the proposed model is more robust to speech recognition errors.

关键词： Spoken language understanding encoder-decoder focus-mechanism robustness

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encoder-decoder with Atrous Separable Convolution for Semantic Image Segmentation 1

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15th European Conference on Computer Vision (ECCV)

作者： Chen, Liang-Chieh Zhu, Yukun Papandreou, George Schroff, Florian Adam, Hartwig Google Inc Mountain View CA 94043 USA

ISBN: (数字)9783030012342

ISBN: (纸本)9783030012342;9783030012335

Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information. In this work, we propose to combine the advantages from both methods. Specifically, our proposed model, DeepLabv3+, extends DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries. We further explore the Xception model and apply the depthwise separable convolution to both Atrous Spatial Pyramid Pooling and decoder modules, resulting in a faster and stronger encoder-decoder network. We demonstrate the effectiveness of the proposed model on PASCAL VOC 2012 and Cityscapes datasets, achieving the test set performance of 89% and 82.1% without any post-processing. Our paper is accompanied with a publicly available reference implementation of the proposed models in Tensorflow at https://***/tensorflow/models/tree/master/research/deeplab.

关键词： Semantic image segmentation Spatial pyramid pooling encoder-decoder Depthwise separable convolution

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Dual attention guided deep encoder-decoder network for change analysis in land use/land cover for Dakshina Kannada District, Karnataka, India

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ENVIRONMENTAL EARTH SCIENCES 2023年第1期82卷 1-21页

作者： Naik, Nitesh Chandrasekaran, Kandasamy Sundaram, Venkatesan Meenakshi Panneer, Prabhavathy Natl Inst Technol Dept Comp Sci & Engn Mangalore 575025 Karnataka India Natl Inst Technol Dept Comp Sci & Engn Karaikal 609609 Puducherry India Vellore Insitute Technol Sch Informat Technol & Engn Vellore 632014 Tamil Nadu India

The Earth is frequently changed by natural occurrences and human actions that have threatened our environment to a certain extent. Therefore, accurate and timely monitoring of transformations at the surface of the Earth is crucial for precisely facing their harmful effects and consequences. This paper aims to perform a change detection (CD) analysis and assessment of the Dakshina Kannada region, being one of the coastal districts of Karnataka, India. The spatial and temporal variations in land use and land cover (LULC) are being monitored and examined from the data received as LULC maps from the National Remote Sensing Agency, Indian Space Research Organization, India. The time-series data from advanced wide-field sensor (AWiFS) Resourcesat2 satellite as LULC maps (1:250k) are analyzed using a deep learning approach with an encoder-decoder architecture with dual-attention modules for the change analysis. The model provides an overall accuracy and meanIOU(intersection over union) of 94.11% and 74.1%. The LULC maps from 2005 to 2018 (13 years) are utilized to decide the variations in the LULC, including urban development, agricultural variations, vegetation dynamics, forest areas, barren land, littoral swamp, and water bodies, current fallow, etc. The multiclass area-wise changes in terms of percentage show a decline in most LULC classes, which raises a point of concern for the environmental safety of the considered area, which is highly exposed to coastal flooding due to increased urbanization.

关键词： Land use and land cover encoder-decoder Deep learning Dual-attention

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An encoder-decoder model with embedded attention-mechanism for efficient meshfree prediction of slope failure

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INTERNATIONAL JOURNAL OF DAMAGE MECHANICS 2023年第10期32卷 1164-1187页

作者： Chen, Jun Wang, Dongdong Deng, Like Ying, Jijun Xiamen Univ Inst Artificial Intelligence Dept Civil Engn Fujian Key Lab Digital Simulat Coastal Civil Engn Xiamen Peoples R China Xiamen Univ Dept Civil Engn Xiamen 361005 Fujian Peoples R China

The particle-based meshfree methods provide an effective means for large deformation simulation of the slope failure. Despite the advances of various efficient meshfree algorithmic developments, the computational efficiency still limits the application of meshfree methods for practical problems. This study aims at accelerating the meshfree prediction of the slope failure through introducing an encoder-decoder model, which is particularly enhanced by the attention-mechanism. The encoder-decoder model is designed to capture the long sequence character of meshfree slope failure analysis. The discretization flexibility of meshfree methods offers an easy match between the meshfree particles and machine learning samples and thus the resulting surrogate model for meshfree slope failure prediction has a quite wide applicability. In the meantime, the embedding of the attention-mechanism into the encoder-decoder neural network not only enables a significant reduction of the number of meshfree model parameters, but also maintains the key features of meshfree simulation and effectively alleviates the information dilution issue. It is shown that the proposed encoder-decoder model with embedded attention mechanism gives a more favorable prediction on the meshfree slope failure simulation in comparison to the general encoder-decoder formalism.

关键词： Meshfree method large deformation slope failure encoder-decoder attention mechanism

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Deep Quasi-Recurrent Self-Attention With Dual encoder-decoder in Biomedical CT Image Segmentation

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第12期28卷 7195-7205页

作者： Agarwal, Rohit Chowdhury, Arindam Chatterjee, Rajib Kumar Chel, Haradhan Murmu, Chiranjib Murmu, Narayan Nandi, Debashis Natl Inst Technol Dept Comp Sci & Engn Durgapur 713209 India Cent Inst Technol Dept Elect & Commun Engn Kokrajhar 783370 India Diamond Harbour Govt Med Coll Diamond Harbour 743331 India

Developing deep learning models for accurate segmentation of biomedical CT images is challenging due to their complex structures, anatomy variations, noise, and unavailability of sufficient labeled data to train the models. There are many models in the literature, but the researchers are yet to be satisfied with their performance in analyzing biomedical Computed Tomography (CT) images. In this article, we pioneer a deep quasi-recurrent self-attention structure that works with a dual encoder-decoder. The proposed novel deep quasi-recurrent self-attention architecture evokes parameter reuse capability that offers consistency in learning and quick convergence of the model. Furthermore, the quasi-recurrent structure leverages the features acquired from the previous time points and elevates the segmentation quality. The model also efficiently addresses long-range dependencies through a selective focus on contextual information and hierarchical representation. Moreover, the dynamic and adaptive operation, incremental and efficient information processing of the deep quasi-recurrent self-attention structure leads to improved generalization across different scales and levels of abstraction. Along with the model, we innovate a new training strategy that fits with the proposed deep quasi-recurrent self-attention architecture. The model performance is evaluated on various publicly available CT scan datasets and compared with state-of-the-art models. The result shows that the proposed model outperforms them in segmentation quality and training speed. The model can assist physicians in improving the accuracy of medical diagnoses.

关键词： deep learning encoder-decoder Biomedical CT image segmentation self-attention self-attention U-Net U-Net self-attention U-Net

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PixISegNet: pixel-level iris segmentation network using convolutional encoder-decoder with stacked hourglass bottleneck

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IET BIOMETRICS 2020年第1期9卷 11-24页

作者： Jha, Ranjeet Ranjan Jaswal, Gaurav Gupta, Divij Saini, Shreshth Nigam, Aditya Indian Inst Technol Sch Comp & Elect Engn Mandi Himachal Prades India Indian Inst Technol Jodhpur Dept Elect Engn Jodhpur Rajasthan India

In this paper, we present a new iris ROI segmentation algorithm using a deep convolutional neural network (NN) to achieve the state-of-the-art segmentation performance on well-known iris image data sets. The authors' model surpasses the performance of state-of-the-art Iris DenseNet framework by applying several strategies, including multi-scale/ multi-orientation training, model training from scratch, and proper hyper-parameterisation of crucial parameters. The proposed PixISegNet consists of an autoencoder which primarily uses long and short skip connections and a stacked hourglass network between encoder and decoder. There is a continuous scale up-down in stacked hourglass networks, which helps in extracting features at multiple scales and robustly segments the iris even in an occluded environment. Furthermore, cross-entropy loss and content loss optimise the proposed model. The content loss considers the high-level features, thus operating at a different scale of abstraction, which compliments the cross-entropy loss, which considers pixel-to-pixel classification loss. Additionally, they have checked the robustness of the proposed network by rotating images to certain degrees with a change in the aspect ratio along with blurring and a change in contrast. Experimental results on the various iris characteristics demonstrate the superiority of the proposed method over state-of-the-art iris segmentation methods considered in this study. In order to demonstrate the network generalisation, they deploy a very stringent TOTA (i.e. train-once-test-all) strategy. Their proposed method achieves $E_1$E1 scores of 0.00672, 0.00916 and 0.00117 on UBIRIS-V2, IIT-D and CASIA V3.0 Interval data sets, respectively. Moreover, such a deep convolutional NN for segmentation when included in an end-to-end iris recognition system with a siamese based matching network will augment the performance of the siamese network.

关键词： image segmentation iris recognition feature extraction image coding decoding convolutional neural nets entropy codes image classification image restoration image matching siamese matching network salient iris features pixel-level iris segmentation network stacked hourglass bottleneck nonregular reflections deep convolutional neural network stacked hourglass network cross-entropy loss pixel-to-pixel classification loss deep convolutional NN encoder-decoder image segmentation performance content loss optimisation hyper-parameterisation Iris-DenseNet framework iris image data sets iris ROI image segmentation algorithm biometric segmentation research multiscale-multiorientation training Pix-SegNet train-once-test-all strategy TOTA strategy CASIA V3 0 interval data sets IIT-D interval data sets UBIRIS-V2 interval data sets

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Context prior-based with residual learning for face detection: A deep convolutional encoder-decoder network

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2020年 88卷

作者： Zhou, Zexun He, Zhongshi Jia, Yuanyuan Du, Jinglong Wang, Lulu Chen, Ziyu Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Hanshan Normal Univ Coll Comp & Informat Engn Chaozhou 521041 Peoples R China Chongqing Med Univ Coll Med Informat Chongqing 400016 Peoples R China

In the field of security, faces are usually blurry, occluded, diverse pose and small in the image captured by an outdoor surveillance camera, which is affected by the external environment such as the camera pose and range, weather conditions, etc. It can be described as a problem of hard face detection in natural images. To solve this problem, we propose a deep convolutional neural network named feature hierarchy encoder-decoder network (FHEDN). It is motivated by two observations from contextual semantic information and the mechanism of multi-scale face detection. The proposed network is a scale-variant style architecture and single stage, which are composed of encoder and decoder subnetworks. Based on the assumption that contextual semantic information around face being auxiliary to detect faces, we introduce a residual mechanism to fuse context prior-based information into face feature and formulate the learning chain to train each encoder-decoder pair. In addition, we discuss some important factors in implement details such as the distribution of training dataset, the scale of feature hierarchy, and anchor box size, etc. They have some impact on the detection performance of the final network. Compared with some state-of-the-art algorithms, our method achieves promising performance on the popular benchmarks including AFW, PASCAL FACE, FDDB, and WIDER FACE. Consequently, the proposed approach can be efficiently implemented and routinely applied to detect faces with severe occlusion and arbitrary pose variations in unconstrained scenes. Our code and results are available on https://***/zzxcoder/EvaluationFHEDN.

关键词： Context prior Residual learning Deep convolutional neural network encoder-decoder Face detection

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Quick large-scale spatiotemporal flood inundation computation using integrated encoder-decoder LSTM with time distributed spatial output models

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JOURNAL OF HYDROLOGY 2024年 634卷

作者： Wei, Guozhen Xia, Wei He, Bin Shoemaker, Christine Dalian Maritime Univ Coll Transport Engn Dalian 116026 Peoples R China Natl Univ Singapore Dept Ind Syst Engn & Management Singapore 117576 Singapore Campus Res Excellence & Technol Enterprise CREATE Energy & Environm Sustainabil Megac E2S2 Phase 2 Singapore 138602 Singapore Politecn Milan Dept Elect Informat & Bioengn I-20133 Milan Italy Dalian Univ Technol Sch Hydraul Engn Dalian 116024 Peoples R China Natl Univ Singapore Dept Civil & Environm Engn Singapore 117576 Singapore

Accurate spatiotemporal flood simulations are essential for making informed decisions regarding flood release in affected regions, such as flood detention areas. Traditional spatiotemporal flood simulation approach uses partial differential equation (PDE) models (or physics-based models), which need high computational time. Although many machine learning (ML) models for inundation are increasingly being used to emulate the PDE models to address this issue, utilizing conventional ML models to achieve large-scale spatiotemporal flood prediction (i.e., simulation output in tens of thousands of grids and time steps over the whole flood event) remains a significant challenge. Therefore, we developed a new inundation model (IM) using encoder-decoder long short-term memory (ED-LSTM) with Time Distributed Spatial output model (ED-LSTM-TDS) that can acquire accurate spatially distributed flood information more rapidly. In the new IM framework, each ED-LSTM-TDS is built to simultaneously generate output for multiple (K) cell grids and multiple ED-LSTM-TDS models are built for prediction at all grids of entire PDE model. This study is the first of its kind to employ the ED-LSTM-TDS method to address spatiotemporal flood inundation simulation problems for flood detention areas. A 1994 km2 flood detention area in northeastern China was used as a case study. ED-LSTM-TDS exhibited better performance in predicting flood characteristics (e.g., water depth, velocity) than alternative methods, including ordinary LSTM, artificial neural network (ANN), and multiple linear regression (MLR). In addition, we investigated the trade-off relationship between the accuracy of flood characteristic prediction and the computation time of the proposed model by considering different numbers of K cell grids in each ED-LSTM-TDS model. The final proposed inundation model could accurately predict the spatiotemporal flood characteristics within 1.5 min to acquire the same information that required approx

关键词： encoder-decoder LSTM Physics-based model Flood inundation prediction Machine learning

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