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Adaptive Wavelet Techniques for Pattren Analysis

Adaptive Wavelet Techniques for Pattren Analysis

引用

Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, IEEE International Maghreb Meeting of the conference on

作者： Munira H. Abdallatif Ahmed E. Rgibi Amany K. Alarbish Sana A. Abdaljlil Abdallah A. Oshah Faculty of Engineering Sabratha University Sabratha Libya

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

Wavelet analysis, a computational method, decomposes data into frequency components, analyzed by scale, effective for signals with discontinuities. Originating from diverse fields like mathematics, physics, and engineering, wavelet theory finds applications in image compression, radar, and more. Using scalable wavelet functions, it allows precise signal reconstruction, differing from constant scale methods. This study compares adapted and traditional wavelets, emphasizing Finite Impulse Response (FIR) efficiency. Applying these wavelets for pattern recognition, particularly in spike detection in EEG data, highlights their versatility in complex signal processing.

关键词： Wireless communication Filters Tensors Finite impulse response filters Radar imaging Wavelet analysis signal reconstruction

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Large-Scale Blind Face Super-Resolution via Edge Guided Frequency Aware Generative Facial Prior Networks

Large-Scale Blind Face Super-Resolution via Edge Guided Freq...

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14th Annual Summit and conference of the Asia-Pacific-signal-and-Information-processing-Association (APSIPA ASC)

作者： Cheng, xi Siu, Wan-Chi Yang, Jian Caritas Inst Higher Educ Hong Kong Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (数字)9786165904773

ISBN: (纸本)9786165904773

Large-scale blind face super-resolution is a significant image processing task with high practical value and wide applications. The task is more challenging than conventional image super-resolution since the degradation is more complex and the amount of useful information is very small inside the image. To address this problem, we propose Edge-guided Frequency-aware Generative Prior Network (EFGPN), a super-resolution method based on edge guidance, frequency constraint, and generative priors. The proposed edge guidance could aid the network in retaining more structural information. Frequency constraints suppress artifacts while allowing the network to generate good features across all frequency bands. The use of generative priors enables the network to produce photorealistic faces. The proposed EFGPN can blindly super-resolve low-resolution faces to high-quality faces. Experimental results indicate that our proposed EFGPN outperforms the state-of-the-arts under subjective and objective evaluations.

关键词： Degradation image edge detection Source coding Superresolution Information processing Reproducibility of results Task analysis

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A Comparative Study of Deep Learning Models for image Super-Resolution Across Various Magnification Levels

A Comparative Study of Deep Learning Models for Image Super-...

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2024 IEEE International conference on Future Machine Learning and Data Science, FMLDS 2024

作者： Soni, Jayesh Gurappa, Subhash Upadhyay, Himanshu Applied Research Center Florida International University Miami United States Florida International University Department of Electrical and Computer Engineering Miami United States

ISBN: (纸本)9798350391213

image Super-Resolution (SR) is a vital image processing technique aimed at improving the resolution of lowresolution images to generate high-resolution images, which is essential in applications such as medical imaging, forensic analysis, satellite imagery, etc. Despite its significance, there are still issues with existing SR techniques, such as handling unknown degradation, generalizing across various domains, and recovering the fine details, especially at high scaling factors. Traditional approaches like interpolation and reconstruction-based methods produce poor results, which leads to the use of deep learning models that have demonstrated notable advancements. However, problems like computational complexity, performance drop on real-world data, and dependency on fixed scaling factors remain. In this research, we conduct a comparative analysis of SR models, namely Super-Resolution Convolutional Neural Networks (SRCNN), SwinIR, and Real-ESRGAN, across multiple image domains (e.g., Animals, Cars, Faces, Humans, Landscapes, Ships) using different scaling factors (e.g., x 2, x 3, x 4) and metrics like Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), Learned Perceptual image Patch Similarity (LPIPS), Objective Difference image (ODI) Score, Tenengrad sharpness and Laplacian Variance. This approach helps us better understand the performance of various deep-learning models, guiding further improvements to ensure they are efficient, robust, and applicable to real-world scenarios. © 2024 IEEE.

关键词： Convolutional neural networks

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Design of Accurate, Power and Area Efficient 16x16 Multiplier using Approximation Techniques

Design of Accurate, Power and Area Efficient 16X16 Multiplie...

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2024 IEEE International conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Sudhakar, C.Venkata Divya, Lanka Reddy, M.Sharath Kumar Tharuni, Kare Nageswar, K. Tirupati India

ISBN: (纸本)9798350382693

Effective multiplication is necessary in high-power computer applications like signal and image processing. Even though approximate arithmetic compromises output precision, it presents an achievable path towards designing energy-efficient digital systems with minimal environmental impact. The paper outlines the design of approximate multipliers, utilizing both conventional and approximate Half Adders & Full Adders by strategically placing for adding partial products at the Most Significant Bit (MSB) positions and OR gates are utilized to add the partial products at the Lower Significant Bit (LSB) positions. In addition, this research paper shows the 16x16 multipliers of unsigned and signed by incorporating clock gating for enhanced power reduction. The Verilog HDL implementation the proposed multiplier includes simulations using Artix-7 AC701 FPGA on xilinx VIVADO design tool. The results found that an enhance accuracy while potentially reducing power usage by 11.8% and 15.8%. © 2024 IEEE.

关键词： Clocks

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Accelerating Traditional Object Detection Methods on Sophisticated Embedded Systems 4th

Accelerating Traditional Object Detection Methods on Sophist...

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4th International conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, Thierry Chouireb, Fatima Ecole Normale Super Laghouat Dept Phys Lab Appl Sci & Didact Laghouat Algeria Univ Amar Telidji Laghouat Dept Elect Telecommun Signals & Syst Lab Laghouat Algeria Univ La Rochelle Dept Comp Sci Lab Math Image & Applicat La Rochelle France

ISBN: (纸本)9783031821523;9783031821530

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. Therefore, special hardware that can acquire, control, or execute in parallel is required. These specialized hardware systems include Digital signal Processors DSPs, Field Programmable Gate Arrays FPGAs, Visual processing Units VPUs, Tensor processing Units TPUs, Neural processing Units NPUs or Graphics processing Units GPUs. This work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. This single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. Computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4x faster than the CPU for the HOG method and 2x for the Haar Cascade Classifier method.

关键词： Object detection Machine Learning Haar Cascade HOG Local feature descriptors GPU implementation OpenCV CUDA SoC Tegra x1 Jetson Nano

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MERGED U-NET FOR BONE TUMORS x-RAY imageS SEGMENTATION 29

MERGED U-NET FOR BONE TUMORS X-RAY IMAGES SEGMENTATION

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IEEE International conference on image processing (ICIP)

作者： xie, Zhaozhi Zhao, Keyang Yan, xu Wu, Shenghui Mei, Jiong Lu, Hongtao Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Affiliated Peoples Hosp 6 Dept Orthoped Surg Shanghai Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Bone tumors x-ray image segmentation is crucial for many medical image processing applications such as x-ray image enhancement, lesion diagnosis, etc. In this paper, we propose a new topological structure of u-net, named merged u-net, for bone tumors x-ray image segmentation. We attach a top-down merged branch to the vanilla encoder-decoder network, enhancing the hierarchical feature aggregation. In the merged path, a multi-feature aggregation block, named merged gate, is proposed for better localization of lesion area. According to the attention matrix, the proposed merged gate can generate a reconstructed feature that contains both low-level information and high-level information. Then we incorporate the reconstructed feature with the small scare feature cued with a local feature fusion method, achieving multi-scare feature aggregation. Additionally, we propose a new x-ray image dataset for bone tumors segmentation, which consists of 88 benign images and 217 malignant images, provided with corresponding segmentation masks. Experimental results demonstrate that the proposed merged u-net outperforms other u-net based medical segmentation methods on the proposed x-ray image dataset.

关键词： Bone tumors Merged u-net Merged gate Hierarchical feature aggregation

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3D Reconstruction of Coronary Artery Vessels from 2D x-Ray Angiograms and Their Pose's Details 30

3D Reconstruction of Coronary Artery Vessels from 2D X-Ray A...

引用

30th IEEE signal processing and Communications applications conference (SIU)

作者： Yavuz Uluhan, Gulay Gedik, Osman Serdar Ankara Yildirim Beyazit Univ Bilgisayar Muhendisligi Ankara Turkey

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450928

Cardiovascular diseases are the most common diseases in our age and are known to be the cause of one-third of the mortality rates in the world unless they are treated. The core priority for the treatment of these diseases is to make the correct diagnosis. Although there are lots of methods such as computer tomography angiography (CTA) and magnetic resonance angiography (MRA) in the diagnosis of cardiovascular diseases, the most used method for diagnosis is the x-RAY angiography. x- RAY angiography images provide 2D vein images and the next step for a complete diagnosis is that surgeons/doctors interpret these images through their experiences, make a diagnosis and determine the treatment method. In this paper, it has been studied to reconstruct 3D synthetic coronary artery vessels tree by using synthetic segmented 2D x-ray angiography images and the pose values of these images with a view to providing early treatment of cardiovascular diseases as a result of early diagnosis and facilitating the life of doctors. In order to obtain a 3D coronary vessel, the fully connected convolutional neural networks model in which we have input the synthetically prepared segmented 2D x-ray vessel images and their pose values and whereby obtained a 3D vessel image has been reconstructed. This model has been introduced for the first time in the literature in this research. The synthetic 3D vessels tree has been successfully reconstructed as a result of the tests performed with the segmented synthetic data. As a result of this work, it is aimed to decrease the mortality rates related to wrong or late diagnosis in cardiovascular diseases.

关键词： Fully Connected Convolutional Neural Networks Coronary Artery Vessel Tree x-Ray Angiography

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Blur Detection for Surveillance Camera System

Blur Detection for Surveillance Camera System

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14th Annual Summit and conference of the Asia-Pacific-signal-and-Information-processing-Association (APSIPA ASC)

作者： Pan, Yikun Tsang, Sik-Ho Chan, Yui-Lam Lun, Daniel P. K. Hong Kong Polytech Univ Hong Kong Peoples R China Ctr Adv Reliabil & Safety Hong Kong Peoples R China

ISBN: (数字)9786165904773

ISBN: (纸本)9786165904773

Surveillance cameras, which are often placed in unconstrained environments, can be tampered with due to many environmental and human factors. It results in degraded surveillance videos and affects the subsequent smart applications that make use of the videos in decision-making. Blur anomaly is one of the most typical problems in those videos, which have the target objects in the videos significantly blurred or partially occluded. Such blur anomalies should be detected as soon as possible to ensure the integrity of the video data. In this study, a novel self-supervised blur detection model is proposed. The model focuses on the detection of four commonly found blur anomalies in surveillance videos. They are the natural blur, defocus blur, dirt blur, and spray-paint blur. By using the self-supervised learning method, we can fully make use of the abundant positive samples to improve detection accuracy. Since, in many situations, camera anomaly detection needs to be carried out with edge devices, we also propose a compressed residual network, which incorporates a color attention module, to reduce the model complexity so that it can be applied to edge applications. Our experimental results show that the proposed model significantly improves over the existing approaches in terms of complexity and accuracy.

关键词： Training image color analysis Surveillance image edge detection Self-supervised learning Cameras Complexity theory

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Enhancing defocus blur in three-dimensional scenes via high-speed optical axial scanning and deep learning 2

Enhancing defocus blur in three-dimensional scenes via high-...

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2nd International Academic conference on Optics and Photonics, IACOP 2024

作者： Li, Zilong Tang, Chenxi Dong, Jiaqing Liu, xuan Lin, Jiabin Liu, Qiegen Song, xianlin School of Information Engineering Nanchang University Nanchang330031 China Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications Nanchang University Nanchang330031 China

ISBN: (数字)9781510688827

ISBN: (纸本)9781510688810

Mitigating image defocus blur induced by the limited depth of field in imaging systems and reconstructing the three-dimensional depth of scenes represents a significant hurdle. In this paper, a method for enhancing defocus blur in three-dimensional scenes via high-speed optical axial scanning and deep learning was proposed. The optical axial scanning system functions by synchronizing a liquid electronically tunable lens and a CCD camera to capture multi-focus image sequences. The system efficiently captures real-world images with a fast acquisition time of ~50 ms per image. The multi-focus image sequences are then fused to all-in-focus images. The multi-focus images and all-in-focus images (ground truth) serve as the dataset for the deep learning neural network. Defocused images can be reconstructed using the trained deep learning network. Furthermore, by establishing a direct relationship between the focal depth and the driving current applied to the liquid electronically tunable lens, the three-dimensional depth of the scene can be rapidly determined. Experimental results validate the feasibility and effectiveness of this method. It is expected to expand the applications of image defocus enhancement in fields such as medical imaging, 3D shape measurement, and augmented reality. © 2025 SPIE.

关键词： CCD cameras

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Improved Prototypical Networks for Remote Sensing Scene Classification

Improved Prototypical Networks for Remote Sensing Scene Clas...

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2023 International conference on image, signal processing, and Pattern Recognition, ISPP 2023

作者： Zhou, Yujin Zhang, xiang Li, Jie Wang, Guoqing xie, Yizhuang Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing100081 China The Shanghai Aerospace Electronic Technology Institute Shanghai201109 China

ISBN: (纸本)9781510666351

In practical applications, remote sensing (RS) scene classification faces data shift problems, including novel class and data discrepancy problems. Due to these problems, it is difficult to obtain representative and discriminative features. Therefore, we propose improved prototypical networks (IPN) based on few-shot learning to solve data shift problems in RS scene classification. First, a novel and effective scheme is proposed, which is to introduce Vision Transformer (ViT) pre-trained on a large-scale dataset as the feature extractor of the prototypical networks. Based on the meta-task training framework, IPN can adapt well to RS scene classification tasks and obtain representative features. In addition, a novel loss function called self-distillation-based prototype loss is designed to obtain discriminative features by introducing inter-sample self-distillation and inter-layer self-distillation methods. Extensive experiments are conducted on several public RS scene datasets. Compared with the existing methods, the proposed method achieves an improvement of 4.18%-21.42%. Results demonstrate that the proposed method can effectively solve the data shift problems in RS scene classification. © 2023 SPIE.

关键词： Distillation

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