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Siamese Network for Single image Rain Removal 2020

Siamese Network for Single Image Rain Removal

引用

Proceedings of the 2020 4th International conference on Vision, image and signal processing

作者： Yuxiang Xu Guomin Sun Jinsong Leng University of Electronic Science and Technology of China Sichuan Radio and TV University China University of Electronic Science and Technology of China China

ISBN: (纸本)9781450389532

this work focuses on removing rain streaks from the single image using the Siamese network. the Siamese network with an unique structure, where the inputs are paired images, are utilized to obtain the discriminative features of the input. We explore a method using the Siamese neural network to learn the difference between rainy images and clean images. the convolutional architecture is used for feature learning. Once the network has been tuned, the key feature of the rain is learned, then the rain streaks can be removed. By training the network on synthetic data, our framework can be generalized in the real rainy image tests. Numerical experiments show that the proposed method performs well in removing rain for the synthetic rainy images in terms of visual and quantitative measures.

关键词： Convolutional Network Rain Removal Siamese Network

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the applications of the new Chinese Remainder theorems for three moduli sets

The applications of the new Chinese Remainder Theorems for t...

引用

Canadian conference on Electrical and Computer Engineering (CCECE)

作者： Wei Wang M.N.S. Swamy M.O. Ahmad Yuke Wang Centre for Signal Processing and Communications Department of Electrical and Computer Engineering Concordia University Montreal QUE Canada

In this paper, we apply the New Chinese Remainder theorem I (New CRT-I) to design the residue-to-binary converters for all the recently introduced three moduli sets M/sub 1/={22/sup n/+1,2/sup n/+1,2/sup n/-1}, M/sub 2/={2/sup n/,2/sup n/-1,2/sup n-1/-1}. M/sub 3/={2/sup n/,2/sup n/+1,2/sup n/-1}, and M/sub 4/=(2n, 2n+1, 2n-1). For all these moduli sets, the converters based on the New CRT-I are consistently faster and require less hardware, compared to the previous residue-to-binary converters which are designed based on the traditional Chinese Remainder theorem.

关键词： Cathode ray tubes Hardware Digital arithmetic image converters Dynamic range Application software signal processing signal design Digital signal processing Fast Fourier transforms

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Detection of Spinal Fractures Using image Enhancement Method and Comparison of PSNR and MSE Matric Based on CT-Scan images

Detection of Spinal Fractures Using Image Enhancement Method...

引用

Electronic and Electrical Engineering and Intelligent System (ICE3IS), International conference on

作者： Sri Rahmawati Iskandar Fitri Gunadi Widi Nurcahyo Information System Department Universitas Putra Indonesia YPTK Padang Indonesia Information Technology Department Universitas Putra Indonesia YPTK Padang Indonesia

ISBN: (数字)9798350378368

ISBN: (纸本)9798350378375

this study explores the enhancement of diagnostic capabilities in Computed Tomography (CT) scans for detecting spinal fractures through advanced image processing techniques. the vertebral column's crucial role in physiological functions across all age groups necessitates precise imaging to identify impairments causing severe pain. the research focuses on preprocessing methods, particularly the cropping of the lumbar region to reduce intrinsic noise and improve CT image clarity. Advanced enhancement techniques such as sharpening and contrast stretching are applied to augment image quality, facilitating the precise identification of spinal fractures. the method used in this research is named the image enhancement method which consists of three sub-methods, namely the enhancement method followed by the sharpening method and the linear contrast stretching method (LCM). Next, we compared and evacuated using Peak signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE) metrics, comparing the enhanced images to the original scans. Results indicate that linear contrast stretching produces images with darker intensities and a broader intensity range, significantly improving image quality. Notably, the contrast-stretched image numbered ten achieved a PSNR of 22.5122 dB and an MSE of 0.074884, demonstrating superior enhancement. these findings highlight the potential of advanced image processing techniques to substantially improve CT imaging for spinal fracture diagnosis, offering a valuable tool for enhancing clinical outcomes.

关键词： image quality Measurement PSNR Pain Computed tomography Noise Physiology image enhancement signal resolution Medical diagnostic imaging

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Analysis of Malignant Brain Tumor Automated Detection by Artificial Intelligence and MRI Dataset

Analysis of Malignant Brain Tumor Automated Detection by Art...

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signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Jason Wang Srilaya Bhavaraju Middlesex School Concord Massachusetts United States Massachusetts Institute of Technology Cambridge Massachusetts United States

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

A brain tumor is a growth of abnormal cells around the brain. Brain tumors can be benign (not cancerous) or malignant (cancerous). Whether benign or malignant, brain tumors can damage surrounding tissues in the brain. In addition, tumors become increasingly harmful as they grow and their impact can depend on their location. For example, tumors found in the cerebellum can affect movement and tumors found in the optic nerve can affect vision. In this research we explored classifying malignant and benign tumors using artificial intelligence (AI) analysis with CNN, KNN, logistic regression, and stacking model. To accomplish this goal, we analyzed a dataset of MRI images containing images of both malignant and benign brain tumors. Furthermore, we increased the accuracy of malignant brain tumor detection by augmenting this dataset with information about lobe location of the tumors.

关键词： Analytical models Accuracy image color analysis Malignant tumors Magnetic resonance imaging Brain tumors Benign tumors Brain modeling Data models Cancer

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Slag Detection System Based on Vibration Using Chaos and Nerve Cell theory

Slag Detection System Based on Vibration Using Chaos and Ner...

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the 4th International conference on Continuous Casting of Steel in Developing Countries

作者： TIAN Lu~(1,2) HUANG Yu-jun~(1,2) HUANG Qi-cai~(1,2) 1.Ramon Science and Technology Co., Ltd, Hengyang 421001, Hunan 2. Beijing Research Institute of Ramon, Beijing 100080

At the process of molten steel casting, outflow slag must be detected to improve the quality of steel product and continuous casting efficiency. After lots of investigation of home and aboard slag detection technology, data analysis and data summarization, the dissertation brings forward the slag detection system of the casting state of steel by analyzing the vibration signal of shroud operation arm using chaos and nerve cell method. the collection and process of molten steel vibration signal and the method of slag characteristic parameter identification has been researched deeply, and basic principles of the application of neural network in casting state recognition are expounded. And the frame and function design of slag detection system are all briefly introduced. the results testing in Xiang Steelworks proved the effectiveness of the slag detection system, which can satisfy the demand for real-time, improve pure measure of molten steel and continuous casting efficiency and create benefit for the steelworks evidently.

关键词： chaos theory neural network slag detection wavelet analysis vibration signal processing

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3D Object Reconstruction Using Sequentially Activated Multiple Depth Cameras

3D Object Reconstruction Using Sequentially Activated Multip...

引用

IEEE signal processing and Communications applications (SIU)

作者： Esra Tunçer Şevket Gümüştekin İzmir Yüksek Teknoloji Enstitüsü İzmir Türkiye

ISBN: (数字)9781728172064

ISBN: (纸本)9781728172071

In this study, 4 depth cameras (Kinect v1) were placed around the object in order to obtain the 3-dimensional structure of an object. Computer bandwidth and interference problems encountered during the operation of multiple Kinects were solved with additional USB 2.0 controllers and electronic shutters. Calibration was performed on infrared images to merge the acquired images. Alignment problems were solved by the Iterative Closest Point method and an alignment with 3.41% error rate was obtained. Poisson Surface Reconstruction was implemented in Meshlab software for 3D modeling objects with different geometrical characteristics.

关键词： Universal Serial Bus Cameras three-dimensional displays Calibration Surface reconstruction Interference image reconstruction

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Robot Control Using an Inexpensive P300 Based BCI

Robot Control Using an Inexpensive P300 Based BCI

引用

Iranian conference of Biomedical Engineering (ICBME)

作者： Shafie Bahman Mohammad B. Shamsollahi Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology Tehran Iran

ISBN: (数字)9781728156637

ISBN: (纸本)9781728156644

Brain Computer Interfaces (BCI) are an important concept of biomedical engineering because of their ability to improve life conditions for people with different disabilities. Lots of studies have worked on important characteristics of BCI systems, such as speed and accuracy, whereas price is an important aspect too. thus, a low cost BCI system with high accuracy clearly can help more people. Our purpose in this study is to design a P300 based BCI system using a low-priced EEG headset which has an acceptable accuracy. Our final design got a mean real-time accuracy of 93.3% which is comparable to systems with much more expensive hardware.

关键词： Electroencephalography Robots Real-time systems Task analysis Graphical user interfaces Mathematical model Biomedical engineering

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Deep Learning based smart traffic light system using image processing with YOLO v7

Deep Learning based smart traffic light system using Image P...

引用

International conference on Circuits, Communication, Control and Computing

作者： Avinash Padmakar Rangari Ashwini Ravindra Chouthmol Chaitanya Kadadas Prashant Pal Shanshank Kumar Singh Dep of ESE National Institute of Electronics & Information Technology Aurangabad India

India is home to 10% of all traffic deaths worldwide and has the second-largest road network in the world. Moreover, in smart cities, traffic congestion, pollutants, and noise pollution have increased due to a constant rise in vehicle kinds, technical problems with traffic signal management equipment, and inefficient road traffic management. Despite the fact that current traffic control systems rely on fixed time-based techniques, conventional traffic control systems are unable to manage the complicated traffic flow at junctions. Roadblocks increase mileage, increase transport costs, and pollute the air in addition to adding to the driver's stress and further delays. therefore, we designed a smart traffic light management system employing the recently launched YOLO V7. the new version V7 of the YOLO algorithm outperforms all previous object detection models in both speed and accuracy. As it is the fastest and most accurate real-time object detection model hence it is the best algorithm to deploy in traffic controlling system. YOLO V7 is +120 % faster than other previous models and shows the best speed to accuracy balance.

关键词： Training Visualization Roads Surveillance Object detection Detectors Traffic control

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Parallel CNN three-phase Inverter Open-Circuit Fault Diagnosis based on STFT and GADF images

Parallel CNN three-phase Inverter Open-Circuit Fault Diagnos...

引用

Electronic Information Engineering and Computer Science (EIECS), 2021 International conference on

作者： Yao Liu Yuhong Wu Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

To apply the advantages of deep learning in 2D image recognition to the fault diagnosis of three-phase inverters, this paper proposes a Parallel Convolutional Neural Network (CNN) model based on short-time Fourier transform (STFT) and Gramian Angular Difference Field (GADF) images. Firstly, the one-dimensional voltage signal output by the inverter is converted into a two-dimensional image using STFT and GADF methods. then, the transformed image data is input into the dual-channel parallel CNN model, where convolution and pooling operations are carried out in the two CNN branches to extract multi-level features of the fault signal. Finally, through feature fusion and fully connected layers, effective classification and diagnosis of inverter open-circuit faults are achieved. Experimental results show that the classification accuracy of the proposed method exceeds 97% under different noise levels. Compared to other methods, the proposed approach demonstrates significant advantages in accuracy and reliability, with strong anti-interference ability and robustness when processing noisy data.

关键词： Fault diagnosis Accuracy Voltage Feature extraction Inverters Robustness Data models Convolutional neural networks Noise level signal to noise ratio

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A Novel image Deep Learning for Recognition of Silicon Photonics Redistribution Layer

A Novel Image Deep Learning for Recognition of Silicon Photo...

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Electronic Communications, Internet of things and Big Data (ICEIB), International conference on

作者： Jen-Kuang Fang Chao-Kai Hung Peng Yang Wen-Long Lu Chien-Wei Chang Advanced Semiconductor Engineering Inc. Kaohsiung Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Due to the design requirements for miniaturization and high-density placement of silicon photonics (SiPh) components in integrated circuit (IC) packaging, the system in package (SiP) process and equipment have become important but challenging. Automatic optical inspection (AOI) detection equipment in IC packaging lines is also important in identifying redistribution layer (RDL) anomalies that affect signal transmission. If the AOI misrecognition rate is too high, the production yield and overall equipment performance are lowered. To solve the above problems, we proposed a novel Pix2Pix generative adversarial network (GAN) algorithm based on machine learning methods. the new feature extraction method has demonstrated highly sensitive image detection capabilities after processing tens of thousands of huge SiPh RDL image databases.

关键词： Deep learning System-in-package image recognition Production Generative adversarial networks Silicon photonics Optical imaging

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