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Conference on signal processing, Sensor/Information Fusion, and Target Recognition XXXII

作者： Cakir, Serdar Sofu, Bugra Roketsan Inc PK 30 Elmadag TR-06780 Ankara Turkiye

ISBN: (数字)9781510662117

ISBN: (纸本)9781510662100;9781510662117

Quality assessment of digital images plays an important role in modeling, implementation and optimization of image and video processing applications. One of the most popular methods in image quality assessment (IQA) is feature based IQA techniques. These feature based image quality assessment (IQA) techniques, which consist of feature extraction and feature pooling phases, extracts features from the images in order to generate objective scores. Various hand-crafted features have been used in the feature extraction phase of the feature based IQA methods. In this work, instead of implementing a hand-crafted feature extraction scheme, automatic feature extraction is utilized by using a pre-trained deep neural network (DNN) inference structure. Feature pooling, which provides mapping between the proposed features and the subjective scores, is carried out by utilizing a fully-connected layer at the end of the network architecture. Experimental results show that the proposed technique obtains promising results for the IQA problem by making use of the generalization capability of deep learning architectures.

关键词： image Quality Assessment Convolutional neural Networks CNN Deep Features

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Improvement of lattice Boltzmann methods based on gated recurrent unit neural network

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signal image AND VIDEO processing 2023年第7期17卷 3283-3291页

作者： Zhao, Yuchen Meng, Fei Lu, Xingtong Univ Shanghai Sci & Technol Dept Syst Sci Shanghai 200093 Peoples R China

Compared with traditional computational fluid dynamics methods, the lattice Boltzmann method (LBM) has the advantages of simple program structure, adaptability to complex boundaries, and easy parallel computation. However, since LBM is an explicit algorithm, there are many iterations in the computation process, which leads to an increase in computation time. In this paper, we improve LBM based on deep learning by combining a convolutional neural network (CNN) and a gated recurrent unit neural network (GRU). Based on previous test data, the CNN module extracts spatial features during the computation, while the GRU processes the corresponding temporal features. Compared with the conventional LBM, this method can significantly reduce the computation time and improve the computational efficiency with guaranteed low Reynolds numbers of 1000 and 2000. At the high Reynolds number of 4000, the prediction error of the proposed method is increasing but still has a better performance. In order to verify the effectiveness and accuracy of the proposed algorithm, an eddying model widely used in the computational fluid field is developed. The proposed method not only has impressive results but also deals with non-stationary processes and steady-state problems.

关键词： Lattice Boltzmann method CG-LBM network Computational fluid Gated recurrent unit neural network

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Unrolled Expectation Maximization for Sparse Radio Interferometric Imaging 32

Unrolled Expectation Maximization for Sparse Radio Interfero...

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32nd European signal processing Conference (EUSIPCO)

作者： Arab, Nawel Mhiri, Yassine Vin, Isabelle El Korso, Mohammed Nabil Larzabal, Pascal Univ Paris Saclay ENS Paris Saclay SATIE F-91190 Gif Sur Yvette France Univ Paris Site MAP5 F-75006 Paris France Univ Paris Saclay CentraleSupelec L2S F-91190 Gif Sur Yvette France

ISBN: (纸本)9789464593617;9798331519773

This paper introduces an unrolled Expectation Maximization (EM) algorithm for sparse image reconstruction from radio interferometric measurements in the presence of a compound Gaussian distribution noise. Traditional model-based reconstruction methods, rooted in inference and optimization fields, provide an initial foundation with theoretical guarantees, but their performance is highly linked to the model accuracy and the choice of hyperparameter values. The popularity of supervised machine learning rose over the last decade, yet they faced hurdles related to interpretability and theoretical foundations. The emergence of unrolled algorithms addresses these limitations by combining the strengths of both approaches. We specifically focus on unrolling a regularized EM algorithm as a feedforward neural network with a residual connection. Experimental results showcase improvements over the iterative EM version.

关键词： Unrolled algorithm expectation maximization sparse image reconstruction radio interferometry

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GSGAN: Learning controllable geospatial images generation

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IET image processing 2023年第2期17卷 401-417页

作者： Su, Xingzhe Lin, Yijun Zheng, Quan Wu, Fengge Zheng, Changwen Zhao, Junsuo Chinese Acad Sci Inst Software 4 Nansi St Zhongguancun Beijing Peoples R China Univ Chinese Acad Sci 19 Yuquan Rd Beijing Peoples R China Chinese Acad Sci Inst Software 4 Nansi St Zhongguancun Beijing 100190 Peoples R China

Compared with natural images, geospatial images cover larger area and have more complex image contents. There are few algorithms for generating controllable geospatial images, and their results are of low quality. In response to this problem, this paper proposes Geospatial Style Generative Adversarial Network to generate controllable and high-quality geospatial images. Current conditional generators suffer the mode collapse problem in geospatial field. The problem is addressed via a modified mode seeking regularization term with contrastive learning theory. Besides, the discriminator network architecture is modified to process global feature information and texture information of geospatial images. Feature loss in the generator is introduced to stabilize the training process and improve generated image quality. Comprehensive experiments are conducted on UC Merced Land Use Dataset, NWPU-RESISC45 Dataset, and AID Dataset to evaluate all compared methods. Experiment results show our method outperforms state-of-the-art models. Our method not only generates high-quality and controllable geospatial images, but also enhances the discriminator to learn better representations.

关键词： feature extraction Geospatial Style Generative Adversarial Network image classification learning (artificial intelligence) Geophysics computing image texture high-quality geospatial images neural nets Optical, image and video signal processing complex image contents Computer vision and image processing techniques natural images global feature information geospatial field remote sensing current conditional generators image recognition generated image quality mode collapse problem Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research texture information geophysical image processing controllable geospatial images generation

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Enhancing pneumonia diagnosis with ensemble-modified classifier and transfer learning in deep-CNN based classification of chest radiographs

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BIOMEDICAL signal processing AND CONTROL 2024年 93卷

作者： Rajeashwari, S. Arunesh, K. Madurai Kamarajar Univ Sri S Ramasamy Naidu Mem Coll Dept Comp Sci Sattur India

Pneumonia is a common and sometimes fatal lung infection that continues to be a major global health concern. The prediction of pneumonia has become a crucial factor in saving people's lives and improving their quality of life. For this purpose, traditional clinical procedures are considered time-consuming. In addition, researchers have used various algorithms to forecast pneumonia due to advances in image processing techniques. However, these algorithms have proven ineffective in terms of feature extraction, which negatively impacts prediction rates. This research aims to predict pneumonia in people worldwide and address the problem of low accuracy. This work introduces a novel method for pneumonia prediction using a deep CNN (Deep Convolutional neural Network) and an InceptionV3 model for feature extraction. Additionally, it introduces an entropy-normalized Neighbourhood Component Analysis (NCA) technique, complemented by Ensemble-Modified Classifiers (EMC) with Naive Bayes, XGBoost, and Random Forest for classification to enhance predictive accuracy. Accurate pneumonia diagnosis is crucial for patient care, but misdiagnoses and delays in diagnosis are not uncommon. This research establishes a robust framework for pneumonia prediction based on deep learning, capable of identifying both normal and atypical pneumonia patterns in medical images. To enhance feature extraction and improve model generalization, the proposed approach combines entropy normalization techniques. This method includes an NCA-based reduction in dimensionality, resulting in more efficient and discriminative feature representations. Furthermore, an ensemble-modified classifier is introduced to refine predictions and improve the model's ability to differentiate between pneumonia and non-pneumonia cases. Experimental results demonstrate that the proposed model surpasses existing methods in terms of accuracy, sensitivity, and specificity. The effectiveness of the proposed system has been confirmed b

关键词： Pneumonia prediction Deep convolutional neural networks InceptionV3 Entropy normalization Neighbourhood component analysis (NCA) XGBoost Random forest Naive Bayes algorithm Chest X-ray images Medical image analysis

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Practical Blind image Denoising via Swin-Conv-UNet and Data Synthesis

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Machine Intelligence Research 2023年第6期20卷 822-836页

作者： Kai Zhang Yawei Li Jingyun Liang Jiezhang Cao Yulun Zhang Hao Tang Deng-Ping Fan Radu Timofte Luc Van Gool Computer Vision Lab ETH ZürichZürich8092Switzerland Computer Vision Lab University of WürzburgWürzburg97074Germany KU Leuven Leuven3000Belgium

While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising,existing methods mostly rely on simple noise assumptions,such as additive white Gaussian noise(AWGN),JPEG compression noise and camera sensor noise,and a general-purpose blind denoising method for real images remains *** this paper,we attempt to solve this problem from the perspective of network architecture design and training data ***,for the network architecture design,we propose a swin-conv block to incorporate the local modeling ability of residual convolutional layer and non-local modeling ability of swin transformer block,and then plug it as the main building block into the widely-used image-to-image translation UNet *** the training data synthesis,we design a practical noise degradation model which takes into consideration different kinds of noise(including Gaussian,Poisson,speckle,JPEG compression,and processed camera sensor noises)and resizing,and also involves a random shuffle strategy and a double degradation *** experiments on AGWN removal and real image denoising demonstrate that the new network architecture design achieves state-of-the-art performance and the new degradation model can help to significantly improve the *** believe our work can provide useful insights into current denoising *** source code is available at https://***/cszn/SCUNet.

关键词： Blind image denoising real image denosing data synthesis Transformer image signal processing(ISP)pipeline

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Deep multi-convolutional stacked capsule network fostered human gait recognition from enhanced gait energy image

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signal image AND VIDEO processing 2024年第2期18卷 1375-1382页

作者： Nithyakani, P. Ferni Ukrit, M. SRM Inst Sci & Technol Dept Comp Technol Kattankulathur 603203 Tamil Nadu India SRM Inst Sci & Technol Dept Computat Intelligence Kattankulathur 603203 Tamil Nadu India

Gait recognition is a well-known biometric identification technology and is widely employed in different fields. Due to the advantages of deep learning, such as self-learning capability, high accuracy and excellent generalization ability, various deep network algorithms have been applied in biometric recognition. Numerous studies have been conducted in this area;however, they may not always yield the expected outcomes owing to the issue of data imbalance in clinical and healthcare industries. To overcome this problem, deep multi-convolutional stacked capsule network fostered human gait recognition from enhanced gait energy image (HGR-DMCSCN) is proposed in this manuscript. Initially, the input images are taken from CASIA B and OU-ISIR datasets. Then the input images are given to preprocessing segment to enhance the superiority of the images based upon contrast-limited adaptive histogram equalization filtering (CLAHEF). Then preprocessed image is given to classification process using deep multi-convolutional stacked capsule network (DMCSCN) that is utilized for human gait detection under various conditions, like normal walking, carrying a bag and wearing a cloth. The proposed HGR-DMCSCN approach is executed in python and its performance is examined under performance metrics, such as F-Score, accuracy, RoC and computational time. Finally, the proposed approach attains 28.70%, 11.87% and 14.79% higher accuracy for CASIA B compared with existing methods.

关键词： Convolutional neural network Capsule network Deep learning Gait energy image Gait biometric recognition

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A hyperdimensional framework: Unveiling the interplay of RBP and GSN within CNNs for ultra-precise brain tumor classification

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BIOMEDICAL signal processing AND CONTROL 2024年第PartB期96卷

作者： Ramalakshmi, K. Rajagopal, Sivakumar Kulkarni, Madhusudan B. Poddar, Harshit PSR Engn Coll Sivakasi 626140 Tamil Nadu India Vellore Inst Technol Sch Elect Engn Vellore 632014 Tamil Nadu India Univ Wisconsin Madison Dept Med Phys Madison WI 53705 USA

This study presents the RBP-CNN model, a convolutional neural network specifically designed for the precise classification of brain tumors in medical imaging. Conventional methods often encounter difficulties in extracting image noise and texture features, which has led to the incorporation of regional binary patterns (RBP) and Gray Standard Normalization (GSN) preprocessing techniques in CNN. The research addresses fundamental inquiries regarding the impact of the model on accuracy, false classifications, and efficiency. The novelty of RBPCNN lies in its distinctive approach to extracting texture features, which involves optimizing pixel values through GSN preprocessing and generating regional binary patterns based on integral images. The objective of this research is to bridge a critical gap by providing a more accurate and efficient model for classifying brain tumors. The key findings reveal the exceptional performance of RBP-CNN, achieving a classification accuracy of 96% with a reduced false classification ratio of 7% across a dataset of 3000 samples. Comparative analyses position RBP-CNN as superior to alternative models in terms of accuracy, false classification rates, and efficiency. The structural insights and hyperparameter values of the model, as well as its application to the FigShare dataset, demonstrate its robustness and scalability. RBP-CNN emerges as an innovative and effective solution, advancing the field of medical image categorization. The findings of this study contribute a novel methodology, paving the way for future exploration in hyperspectral image applications and positioning RBP-CNN as a potential state-ofthe-art tool for medical image analysis.

关键词： Brain Tumor Classification Convolutional neural Network (CNN) Regional Binary Pattern (RBP) Gray Standard Normalization (GSN)

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Tensor-Based Chaotic Convolutional neural Network for Remote Sensing Data Classification 2

Tensor-Based Chaotic Convolutional Neural Network for Remote...

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2nd IEEE International Conference on signal, Information and Data processing, ICSIDP 2024

作者： Chen, Luobing Yin, Junjun Yang, Jian University of Science and Technology Beijing Beijing China Tsinghua University Beijing China

ISBN: (纸本)9798331515669

With the advancement of deep learning techniques, the classification of remote sensing data using artificial neural networks has emerged as a prominent research area. Despite this progress, the emulation of brain structures by traditional artificial neural networks remains at a relatively low level. Therefore, there is an urgent need to explore novel methods that can more effectively simulate human brain functions and enhance algorithmic performance. At the same time, artificial neural networks in image processing require the transformation of raw data into vector form. However, this conversion disrupts the inherent relationships between adjacent positional data in the raw data, resulting in the loss of crucial spatial structural information. To address these issues, we propose a tensor-based chaotic convolutional neural network model for the classification of remote sensing data. Firstly, we constructed a 3D discrete chaotic system, utilizing both Logistic mapping and Tent mapping, to achieve a more uniform iterative distribution and a broader full mapping range. This system was then integrated with convolutional neural networks to devise a novel chaotic convolutional neural network algorithm. By introducing chaotic mechanisms, this algorithm addresses the drawback of neural networks being prone to local minima. Secondly, we established a tensor model for remote sensing data. In contrast to existing methods that convert raw data into vector form, our approach represents raw data in tensor form, thereby preserving the spatial structural information among the three channels of the raw data. Finally, the effectiveness of the algorithm was validated using the NWPU-RESISC45 remote sensing dataset © 2024 IEEE.

关键词： Convolutional neural networks

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R2D2 image RECONSTRUCTION WITH MODEL UNCERTAINTY QUANTIFICATION IN RADIO ASTRONOMY 32

R2D2 IMAGE RECONSTRUCTION WITH MODEL UNCERTAINTY QUANTIFICAT...

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32nd European signal processing Conference (EUSIPCO)

作者： Aghabiglou, Amir Chu, Chung San Dabbech, Arwa Wiaux, Yves Heriot Watt Univ Inst Sensors Signals & Syst Edinburgh EH14 4AS Midlothian Scotland

ISBN: (纸本)9789464593617;9798331519773

The "Residual-to-Residual DNN series for high-Dynamic range imaging" (R2D2) approach was recently introduced for Radio-Interferometric (RI) imaging in astronomy. R2D2's reconstruction is formed as a series of residual images, iteratively estimated as outputs of Deep neural Networks (DNNs) taking the previous iteration's image estimate and associated data residual as inputs. In this work, we investigate the robustness of the R2D2 image estimation process, by studying the uncertainty associated with its series of learned models. Adopting an ensemble averaging approach, multiple series can be trained, arising from different random DNN initializations of the training process at each iteration. The resulting multiple R2D2 instances can also be leveraged to generate "R2D2 samples", from which empirical mean and standard deviation endow the algorithm with a joint estimation and uncertainty quantification functionality. Focusing on RI imaging, and adopting a telescope-specific approach, multiple R2D2 instances were trained to encompass the most general observation setting of the Very Large Array (VLA). Simulations and real-data experiments confirm that: (i) R2D2's image estimation capability is superior to that of the state-of-the-art algorithms;(ii) its ultra-fast reconstruction capability (arising from series with only few DNNs) makes the computation of multiple reconstruction samples and of uncertainty maps practical even at large image dimension;(iii) it is characterized by a very low model uncertainty.

关键词： Computational methods Deep learning Astronomy image processing Aperture synthesis

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