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BM3D Denoising Algorithms for Medical image 35

BM3D Denoising Algorithms for Medical Image

引用

35th conference of Open Innovations Association FRUCT, FRUCT 2024

作者： Alnuaimy, Ahmed N. H. Jawad, Aqeel Mahmood Abdulkareem, Sarah Ali Mustafa, Firas Mahmood Ivanchenko, Svitlana Toliupa, Serhii Al-Rafidain University College Baghdad Iraq Al-Turath University College Baghdad Iraq Al-Noor University College Nineveh Iraq Odessa Medical Institute of the International Humanitarian University Odesa Ukraine Taras Shevchenko National University of Kyiv Kyiv Ukraine

ISBN: (纸本)9789526524610

Background: Medical diagnostic and imaging technology has been fundamentally impacted by the twenty-first-century increase in internet technology, computers, wireless communication, and data storage. However, like with other imaging modalities, medical imaging may be hampered by noise and artefacts, compromising practical diagnostic analysis and potentially posing health hazards. Objective: The study focuses on the BM3D (Block Matching and 3D Filtering) technique, a state-of-the-art method, to combat the noise in medical images. Denoising these images aims to improve the quality of medical diagnoses and reduce associated risks. Methods: Building upon the foundation set by the Non-Local Means (NLM) filtering method, the BM3D technique utilises a patch-based denoising mechanism. Instead of denoising individual pixels, clusters or blocks of pixels are processed collectively to improve the overall image quality. Results: BM3D will exhibit strong performance against impartial thoroughness criteria, making it a prospective stalwart in the denoising realm for medical images. However, certain limitations are identified, like user-supplied noise levels and potential artefacts due to hard thresholding. Conclusion: While BM3D emerges as a powerful denoising tool for medical images, it is imperative to address its limitations further to bolster its efficacy and applicability in real-time diagnostic imaging systems. © 2024 FRUCT.

关键词： Health hazards

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Comparison of Noise Removal Methods for Noisy Lenke Type 1 Idiopathic Scoliosis images 13

Comparison of Noise Removal Methods for Noisy Lenke Type 1 I...

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13th IEEE international conference on Communication systems and Network Technologies, CSNT 2024

作者： Lubis, Julnila Husna Ali, Hasimah Yusof, Mohd Imran Jusman, Yessi Universiti Malaysia Perlis Faculty of Electrical Engineering & Technology Perlis Malaysia School of Medical Sciences Universiti Sains Malaysia Faculty Department of Orthopaedic Penang Malaysia Universitas Muhammadiyah Yogyakarta Faculty of Engineering Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350305463

Improving the quality of medical images is essential for accurate clinical diagnosis. However, medical images are often plagued by various types of noise, posing challenges to the reliability and accuracy of image diagnosis. This study aims to compare Anisotropic, Wiener, Median, Average, and Gaussian filters on noisy images to improve the quality and accuracy of diagnostic results. To better adapt to the complex features in Lenke images, the contrast optimization algorithm was enhanced using Contrast-Limited Adaptive Histogram Equalization (CLAHE). In this study, artificial noise modeling (Gaussian and Quantum noise) was applied to Lenke X-ray images, and a comparative analysis was performed to discover the most effective filtering scheme for a particular type of noise based on various parameters such as MSE, PSNR, SNR, and SSIM. The results unveiled that the Wiener filter was relatively effective for both types of noise. The Gaussian filter also performed satisfactorily. Nonetheless, no filter could be declared the most effective for both types of noise. However, their performance varied according to the type of noise present and the evaluation parameters. © 2024 IEEE.

关键词： signal to noise ratio

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technology research of φ-OTDR fiber Perimeter intrusion System based on CNN-LSTM algorithm 3

Technology research of φ-OTDR fiber Perimeter intrusion Sys...

引用

3rd international conference on Advanced Algorithms and Neural Networks, AANN 2023

作者： Li, Zhixing Wang, Zhengqi Li, Jiahui Yang, Huan Liang, Guodong Hao, Zhihao Wang, Guangyan Tianjin University of Commerce Tian Jin300130 China

ISBN: (纸本)9781510668355

The fiber optic sensing perimeter security system has broad application prospects due to its unique characteristics such as long transmission distance, wide monitoring range, high sensitivity, and strong real-time performance. based on DWT and STFT, this paper extracts the features of the intrusion source signal and uses the CNN-LSTM deep learning algorithm to intelligently classify the signal, improving the separability of disturbance events and accurately judging the intrusion source type. The average accuracy rate of classification can reach more than 96%, with excellent intelligence and low false alarm performance. This technology is a perfect replacement for traditional perimeter alarm systems such as infrared beams and electronic fences, especially applicable for real-time measurement of vibration-related events such as long-distance, omnidirectional, and multi-point illegal intrusion, illegal destruction, and structural damage. In addition, the application of distributed optical fiber sensing system in the industrial chain can improve product quality, ensure production safety, and realize production line *** research will improve the reliability and accuracy of the perimeter security system, improve the public security environment, save social resources, and improve social security. © 2023 SPIE. All rights reserved.

关键词： image recognition

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The Improved Pedestrian Target Detection and Re-recognition Algorithm

The Improved Pedestrian Target Detection and Re-recognition ...

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2023 international conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Shan, Liang Xiong, Qiaohui Li, Kerong Chen, Yangyang Wang, Jun Nanjing University of Science and Technology Automation School Nanjing China

ISBN: (纸本)9798350312492

image processing technology can be applied to detect and recognize the specific pedestrian target. To well detect moving pedestrian targets in complex occlusion scenes, the improved pedestrian detection algorithm based on YOLOv5 and re-recognition algorithm based on foreground mask estimation are proposed in this paper. In the improved pedestrian detection algorithm, aiming at interclass occlusion, a new two-branch fusion detection head is designed, the loss function and sample matching strategy are modified, meanwhile the non-maximum suppression algorithm is optimized. Aiming at in-class occlusion, SoftNMS algorithm is introduced to improve the detection capability in occlusion scene. In the modified pedestrian re-recognition algorithm, to enhance the pedestrian re-recognition rate under intricate backgrounds, HRNetV2 network replaces ResNet50 as the backbone network and three loss functions are optimized. Experimental results show that modified algorithms improves the performance of pedestrian target detection and re-recognition in complex occlusion scenes. © 2023 IEEE.

关键词： signal detection

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Exposure image Correction based on Fuzzy Theory 8

Exposure Image Correction Based on Fuzzy Theory

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8th Annual international conference on Network and Information systems for Computers, ICNISC 2022

作者： Zou, Liangna Wu, Zhan Xi'an Technological University Shanxi Xian China Xiamen University Fujian Xiamen China

ISBN: (纸本)9781665453516

In order to improve the visual quality of the original image, an image fusion and enhancement technology based on fuzzy theory is proposed in this paper. For the overexposed image, the corresponding membership function is designed to realize the fuzzy domain transformation of the original image, remove the noise and strong light interference of the original image and retain the details for the subsequent enhancement of the image contrast. The image fusion algorithm based on Laplace pyramid effectively combines the salient features of the blurred image and can provide high-quality spectral content. After verification, the image obtained by our method is better than the original image, Gamma correction image and histogram equalization image in visual effect, and the image processed by our method has higher definition, information entropy and peak signal-to-noise ratio, which verifies the superiority of our method. © 2022 IEEE.

关键词： image fusion

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Lightweight design and deployment of object detection model for low-light scenes 4

Lightweight design and deployment of object detection model ...

引用

4th international conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Sun, Kangjian Huo, Ju Jiang, Xiaona Gao, Yichao Cai, Chen School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin China China FAW Corporation Limited Changchun China College of Design and Engineering National University of Singapore Singapore Signal and Communication Research Institute China Academy of Railway Sciences Beijing China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Low-light scenes are common in fields such as autonomous driving, which tests the robustness of intelligent systems. This paper tests the performance of object detection in low-light scenes based on the YOLOv8n model. In order to solve the constraint of limited computing power of embedded devices, the YOLOv8n model is adjusted through model pruning and knowledge distillation. The complexity of the model is reduced through these lightweight operations. While ensuring the detection performance as much as possible, the multiply-accumulate operations (MACs) and parameters of the model decreased by about 40% and 27% respectively. The proposed model is evaluated on the Exclusively Dark (ExDARK) dataset and achieves a mean average precision (mAP) value of 0.672. In addition, the proposed model is migrated to the embedded platform NVIDIA Jetson Xavier NX. The deployment process is optimized by multi-process scheduling and multi-thread scheduling. Experiments are also conducted on real data, and the results show that the proposed model can provide a feasible solution for vision-based nighttime autonomous driving. © 2025 SPIE.

关键词： Object detection Deep learning image processing

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A new fringe analysis method based on shared bottom network for single frame 3D reconstruction 15

A new fringe analysis method based on shared bottom network ...

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15th international conference on Information Optics and Photonics, CIOP 2024

作者： Lu, Jin Zhang, Min Jie Li, Mu H. Li, Mu J. Wang, Jin Wu, Pei Y. Sun, Wei Li, Lin School of Communication and Information Engineering XI'AN University of Posts&Telecommunications 618 Chang'an West St. Shaanxi Xi'an710121 China School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an China China Academy of Industrial Internet Electronic city technology research and development center Chaoyang Beijing100020 China College of Engineering Ohio State University ColumbusOH43210 United States Wuhan University of Technology 1178 Heping Avenue Wuchang Hube Wuhan430061 China

ISBN: (数字)9781510686175

ISBN: (纸本)9781510686168

Recently, deep learning-based methods have been employed in optical measurement. The fringe to phase method based on deep learning can achieve high-precision 3D topography measurement and is applied to various optical metrology tasks, including phase extraction, phase unwrapping, fringe order determination, depth estimation, and other crucial steps. However, it appears simplistic to obtain images of each metrological task from a single fringe pattern. This paper proposes a novel network that effectively extracts the semantic features of fringe patterns by incorporating the design architecture of Transformer while retaining the advantages of convolutional networks. The architecture primarily consists of a backbone, decoder, and feature extraction block which enhance the features at different frequencies within a single fringe pattern. The backbone and decoder are specifically designed for wrapped phase prediction tasks. Experimental results demonstrate that the network accurately predicts the wrapped phase from a single fringe pattern. In comparison with previous methods, this paper's approach offers several contributions: an efficient utilization of a new type of encoder for extracting high-level semantic features from fringe patterns;moreover, only a single grayscale image is required as input for the network without relying on color composite images or additional prior information. © 2024 SPIE.

关键词： Fringe analysis Feature extraction Convolution Semantics Network architectures Phase unwrapping 3D modeling Deep learning Phase shifts Projection systems

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Smart Sociolinguistic Intelligent Analysis Framework based on Feature Extraction and Matching of Structural Data 4

Smart Sociolinguistic Intelligent Analysis Framework Based o...

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4th international conference on Smart systems and Inventive technology, ICSSIT 2022

作者： Lin, Wei Wang, Wenpu School of Foreign Languages and International Education Chengdu Technology University Sichuan Chengdu611730 China

ISBN: (纸本)9781665401180

Smart sociolinguistic intelligent analysis framework based on feature extraction and the matching of structural data is designed in this paper. Since data mining has added annotations to the features of the search data, when searching and searching data, it is faster and more accurate than traditional statistical analysis methods. This paper uses the SUSAN features, the method is simple, the anti-noise ability is strong, and the processing speed is fast. The matching similarity measurement should also consider the feature point direction information to integrate the structure features. For target matching, the side direction information can be directly represented by the feature vector formed by the two-directional cross template response, and the framework is then completed. The model is validated through the public data and the collected data from the internet. © 2022 IEEE

关键词： Feature extraction

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Traffic Management Scheduling using image/Video Processing for Smart Cities 4

Traffic Management Scheduling using Image/Video Processing f...

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4th international conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Reddy, K. Venkata Rama Durgaprasad Priya, R. Shanmukha Singh, Pankaj Kishore, V Ravi Devi, B.S. Kiruthika Aditya Engineering College Department of Cse A.P. Surampalem India Cl Educate Ltd. New Delhi India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Nowadays, traffic has been the major problem in many urban regions as the number of vehicles has been increasing rapidly. So, the traffic management at the traffic signals has become a complex issue as using the conventional system does not detect the traffic and it will not check the traffic density at the signals. Even there are no vehicles;the signal will be green for that empty road. Traffic issues in urban areas are road networks, free networks. To address this problem, image and video processing is a method to perform some operations on an image, to enhance or extract. It is a rapidly growing technology and it is a part of an AI. The traffic can be managed by a suitable solution based on the instantaneous images and videos captured by the cameras at the crossroads, traffic signals, etc. The tools used for this system are Raspberry Pi board and OpenCV tool for image and video processing. So many technologies used in this traffic Management control system Consists to control traffic Density, traffic clearance, Computation of Overlapping images, Alert systems and self-learning systems. The paper concludes with a brief discussion of future needs in these important technologies. © 2022 IEEE.

关键词： internet of things

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Automatic Seat Identification System in Smart Transport using IoT and image Processing 3

Automatic Seat Identification System in Smart Transport usin...

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3rd international conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Bhatia, Sandeep Gautam, Devraj Kumar, Surender Verma, Soniya Galgotias University Department of Cse Uttar Pradesh Greater Noida India Department of Ece Adgitm New Delhi India Department of Hss Kiet Uttar Pradesh NCR Delhi India

ISBN: (纸本)9781665453578

In the last few years there is a huge advancement in image processing technology. It can be implemented in wide variety of applications, one of the applications can be applied in automatic seat identification in public transportation systems by detecting and processing digital pictures. Face detection and recognition is a branch of image processing that can be used to detect human face from specific region. In this paper, we are focusing on techniques based on real time human face identification and a webcam can be utilized to capture the digital image to count the number of passengers entering or leaving the public transport via gate and count the passenger gender for calculation of available seat on bus based on gender. For this purpose, the webcam can be deployed at the entrance of public vehicles and connected with Raspberry Pi processor module. This onboard webcam will take pictures of passengers entering or leaving the public vehicle as soon as the vehicle departs. The amount of noise present in the picture can be minimized through the software. 4G communications may be used to transmit data to server and after that the server will utilize face detection technology to process the digital images taken from the vehicles. This IoT-enabled framework then obtains the data of the number of persons entering or leaving the public transport with gender and subsequently processing of images resulting in calculation of the seat vacancy available in vehicle. This framework is efficient in terms of real-time data processing and transmission of data to remote locations regarding the number of passengers traveling at a particular time. In the last part of the paper, security concerns regarding the system will be discussed. The future scope of our framework will also be mentioned in the paper. © 2023 IEEE.

关键词： internet of things

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