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Conference on applications of Digital image processing XLIII

作者： Liang, Hanning Sauer, Tomas Faber, Christian Landshut Univ Appl Sci Lurzenhof 1 Landshut Germany Univ Passau Innstr 41 Passau Germany

ISBN: (数字)9781510638273

ISBN: (纸本)9781510638273

Phase measuring deflectometry (PMD) is a well-established method for determining the topography of specular freeform surfaces. A disadvantage of the classical PMD method, however, is the sequential measurement process it requires at least six camera images of phase-shifted sinusoidal fringe patterns for one measurement. Therefore, for moving objects in industrial production, as well as for non-fixable objects such as the human cornea, the classical PMD evaluation is not suitable any more. To overcome this problem, single-shot methods using single-sideband demodulation have been presented, which allow for a deflectometric measurement based on just one single image capture. However, this kind of evaluation does not work for complex surface geometries that result in broadband fringe patterns, since the phase is only considered globally in the Fourier space. A new single-shot evaluation method for the phase determination using the Continuous wavelet Transform (CWT) is presented. The advantage of the wavelet transform is that the signal can be evaluated locally in both spatial and frequency space, making it possible to measure even complex reflective surfaces in motion. First measurement results are shown and compared to the classic phase-shifting evaluation for a non-moving object. Furthermore, limits and possible enhancements of this new method are discussed.

关键词： Deflectometry Industrial Metrology 3D Metrology Specular Surfaces Single-Shot Measurement

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Monte Carlo Optimization of a Combined image Quality Assessment for Compressed images Evaluation

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TRAITEMENT DU signal 2021年第2期38卷 281-289页

作者： Bouida, Ahmed Khelifi, Mustapha Beladgham, Mohammed Hamlili, Fatima-Zohra Univ TAHRI Mohammed Bechar Informat Proc & Telecommun Lab LTIT Bechar 08000 Algeria

In image processing, using compression is very important in various applications, especially those using data quantities in transmission and storing. This importance becomes most required with the evolution of image quantities and the big data systems explosion. The image compression allows reducing the required binary volume of image data by encoding the image for transmission goal or database saving. The principal problem with image compression when reducing its size is the degradation that enters the image. This degradation can affect the quality of use of the compressed image. To evaluate and qualify this quality, we investigate the use of textural combined image quality metrics (TCQ) based on the fusion of full reference structural, textural, and edge evaluation metrics. To optimize this metric, we use theMonte Carlo optimization method. This approach allows us to qualify our compressed images and propose the best metric that evaluates compressed images according to several textural quality aspects.

关键词： image quality assessment combined FR-IQA texture analysis edge evaluation image wavelet compression

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A Novel Approach for the Detection of Tuberculosis and Pneumonia Using Chest X-Ray images for Smart Healthcare applications

IEEE SENSORS LETTERS

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IEEE SENSORS LETTERS 2023年第12期7卷 1页

作者： Kabi, Subrat Kumar Tripathy, Rajesh Kumar Patra, Dipti Panda, Ganapati Natl Inst Technol NIT Dept Elect Engn Rourkela 769008 India BITS Pilani Dept Elect & Elect Engn Hyderabad Campus Secunderabad 500078 India CV Raman Global Univ Dept Elect & Commun Engn Bhubaneswar 752054 India

The early discrimination of pneumonia and tuber-culosis is difficult for radiologists due to similar pathological symptoms in the chest X-ray images. Therefore, there is a requirement for automated methods to detect and classify these two diseases accurately. This letter proposes the multiscale eigendomain gradient boosting (MEGB)-based approach to detect pneumonia and tuberculosis from chest X-ray images. The discrete wavelet transform is employed to evaluate subbands of chest X-ray images at different decomposition levels. The singular value decomposition (SVD) is utilized in each subband to evaluate singular values, left eigenmatrix, and right eigenmatrix, respectively. The maximum value of each column of both left and right eigenmatrices and singular values for each subband of the X-ray image are used as features. All subband eigendomain feature vectors are concatenated and given to the light gradient boosting model to detect pneumonia and tuberculosis diseases. The performance of the proposed MEGB approach-based detection is evaluated using chest X-ray images from a publicly available database. The suggested MEGB approach has achieved an accuracy value of 96.42%. The suggested approach performs better than the transfer learning and other reported methods to detect pneumonia and tuberculosis using chest X-ray images.

关键词： X-ray imaging Biomedical imaging Tuberculosis Pulmonary diseases Feature extraction Transfer learning Databases Sensor signal processing accuracy light gradient boosting model (LGBM) multiscale eigendomain features pneumonia tuberculosis

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High dimensional Bio medical images denoising using wavelet transform and modified bilateral filter

High dimensional Bio medical images denoising using wavelet ...

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International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)

作者： Muhammad Usama Jabbar Ali Waqar Muhammad Awais Zohaib Mushtaq Muhammad Jamshed Abbass Muhammad Abdul Rehman Faisal Saleem Shehzad Ahmed Ahmed Zubair Jan Department of Electrical engineering National University of Sciences and Technology Islamabad Pakistan University degli Studi di Napoli Parthenope Italy Faculty of Engineering and Applied Science Riphah International University Faculty of Mechanical Engineering Wrocław University of Science and Technology

ISBN: (纸本)9781665460729

Biomedical images consist of salient information that helps in diagnosis of diseases. Significant amount of information is lost during process of transmission due to addition of noisy components especially for Computed Tomography (CT) scan, Ultrasound imaging and Magnetic Resonance Imaging (MRI). image denoising is an important and necessary pre-processing phase in image applications. Numerous techniques have been developed for denoising process over decades. The purpose is to remove noise from images. wavelet transform is current state of the art addition to analyze image denoising; able to achieve improved experimental results than from existing techniques. Generalized Gaussian distribution is considered to model noise. In this article, we introduce mix adaptive thresholding with modified bilateral filter. Simulation results demonstrate that attributes of images are improved. The Qualitative and quantitative assessment proves better denoised results for proposed method using parameters like Peak signal to Noise Ratio (PSNR), Mean Opinion Score (MOS) and Structural Similarity Index (SSIM) between the true and estimated biomedical images.

关键词： wavelet transforms Thresholding (Imaging) PSNR Ultrasonic imaging Computed tomography Magnetic resonance imaging Simulation

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Real-time application based CNN architecture for automatic USCT bone image segmentation

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BIOMEDICAL signal processing AND CONTROL 2022年第PartA期71卷 103123-103123页

作者： Fradi, Marwa Zahzah, El-hadi Machhout, Mohsen Monastir Univ Phys Dept Lab Elect & Microelect Fac Sci Monastir Monastir Tunisia La Rochelle Univ Lab Informat Image & Interact L3i La Rochelle France

Artificial Intelligence (AI) in medical image analysis has achieved excellent success in automatic diagnosis in the same way as clinician, especially in the ultrasound field. In this work, we develop a new segmentation application based on various Convolutional Neural Network (CNN) models for Ultrasonic Computed Tomographic (USCT) images. To evaluate the proposed segmentation system, we use different state-of-the-art models for better segmentation performances to train and test the suggested system. We ensure in this work a USCT data augmentation technique based on the Haar wavelet transform and the improved k-means algorithms. Thus, we offer a free dataset for USCT researchers. Moreover, the proposed CNN system is trained and tested using the networks of Adadelta and Adam optimizers. The whole system is implemented on a CPU and a GPU for complexity analysis. High segmentation accuracy has been achieved using the Adadelta optimizer, reaching 99.24%, 99.19%, 99.13% and 99.10% for VGG-Segnet, VGG-Unet, Fully CNN (FCN)-8 and FCN-32 models, respectively. To obtain better results, we use the Adam optimizer to train and test different architectures, and we obtain more competitive results attaining 99.55%, 99.31%, 99.35% and 99.45% for VGG-Segnet, VGG-Unet, FCN-8 and FCN-32, respectively. The achieved results outperform the state of the art in terms of accuracy and time speed up. Moreover, our proposed CNN segmentation confirms the low computational complexity of the system. In addition, our system proves to be a good candidate for medical real-time applications thanks to its implementation on the GPU.

关键词： USCT VGG-segnet VGG-unet FCN-8 FCN-32 GPU Accuracy

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Veterinary image Enhancement using DWTDCT and Singular Value Decomposition 9

Veterinary Image Enhancement using DWTDCT and Singular Value...

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9th IEEE International Conference on Communication and signal processing, ICCSP 2020

作者： Rajan, Bindhu K Harshan, Hiron M Venugopal, G. APJ Abdul Kalam Technological University NSS College of Engineering Palakkad Kerala India College of Veterinary and Animal Sciences Mannuthy Gynecology Department Thrissur Kerala India APJ Abdul Kalam Technological University NSS College of Engineering Instrumentation and Control Engineering Department KeralaPalakkad India

ISBN: (纸本)9781728149882

As imaging technology advances, one of the unavoidable part of image processing is the image enhancement. image enhancement is to improve the visual quality for specific applications. The proposed algorithm enhances the visual quality of the vaginoscopic images of bitches using Discrete wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD). The input image is decomposed into four frequency bands by using DWT. The low frequency band of the transformed image is again transformed by DCT into low frequency DCT coefficients and higher DCT coefficients. New low frequency band is formed by SVD. Inverse Discrete Cosine Transform (IDCT) is applied to produce the equalized image. Inverse Discrete wavelet Transform (IDWT) of new low frequency band and the other three frequency band are combined to give the enhanced image. Different quality measures such as Root Mean Square Error (RMSE), Peak to signal Noise Ratio (PSNR), Entropy, mean and Standard deviation are used to evaluate the performance of the model. The proposed model is implemented using MATLAB 2017a and obtained an average RMSE of 1.645. PSNR, Entropy, mean and standard deviation values for the proposed model are 44.12dB, 6.57, 103.35, 0.11 respectively. This qualitative analysis shows that proposed method seems to be useful for enhancing veterinary images for further processing. © 2020 IEEE.

关键词： Singular value decomposition

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image inpainting using directional wavelet packets originating from polynomial splines

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signal processing-image COMMUNICATION 2021年 97卷 116334-116334页

作者： Averbuch, Amir Neittaanmaki, Pekka Zheludev, Valery Salhov, Moshe Hauser, Jonathan Tel Aviv Univ Sch Comp Sci Tel Aviv Israel Univ Jyvaskyla Fac Math Informat Technol Jyvaskyla Finland Tel Aviv Univ Sch Elect Engn Tel Aviv Israel

The paper presents a new algorithm for the image inpainting problem. The algorithm uses a recently designed versatile library of quasi-analytic complex-valued wavelet packets (qWPs) which originate from polynomial splines of arbitrary orders. Tensor products of 1D qWPs provide a diversity of 2D qWPs oriented in multiple directions. For example, a set of the fourth-level qWPs comprises 62 different directions. The properties of these qWPs such as refined frequency resolution, directionality of waveforms with unlimited number of orientations, (anti-)symmetry of waveforms and windowed oscillating structure of waveforms with a variety of frequencies, make them efficient in image processing applications, in particular, in dealing with the inpainting problem addressed in the paper. The obtained results for this problem are quite competitive with the best state-of-the-art algorithms. The inpainting is implemented by an iterative scheme, which expands the Split Bregman Iteration (SBI) procedure by supplying it with an adaptive variable soft thresholding based on the Bivariate Shrinkage algorithm. In the inpainting experiments, performance comparison between the qWP-based methods and the state-of-the-art algorithms is presented.

关键词： Iterative methods

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Hardware Implementation for Lower Limb Surface EMG Measurement and Analysis Using Explainable AI for Activity Recognition

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1页

作者： Vijayvargiya, Ankit Singh, Puneet Kumar, Rajesh Dey, Nilanjan Malaviya Natl Inst Technol Dept Elect Engn Jaipur 302017 Rajasthan India Swami Keshvanand Inst Technol Management & Gramot Dept Elect Engn Jaipur 302017 Rajasthan India Malaviya Natl Inst Technol Dept Elect & Commun Engn Jaipur 302017 Rajasthan India JIS Univ Dept Comp Sci & Engn Kolkata 700109 India

Electromyography (EMG) signals are gaining popularity for several biomedical applications, including pattern recognition, disease detection, human-machine interfaces, medical image processing, and robotic limb or exoskeleton fabrication. In this study, a two-channel data acquisition system for measuring EMG signals is proposed for human lower limb activity recognition. Five leg activities have been accomplished to measure EMG signals from two lower limb muscles to validate the developed hardware. Five subjects (three males and two females) were chosen to acquire EMG signals during these activities. The raw EMG signal was first denoised using a hybrid of wavelet Decomposition with Ensemble Empirical Mode Decomposition (WD-EEMD) approach to classify the recorded EMG dataset. Then, eight time-domain (TD) features were extracted using the overlapping windowing technique. An investigation into the comparative effectiveness of several classifiers is presented, although it was hard to distinguish how the classifiers predicted the activities. Having a trustworthy explanation for the outcomes of these classifiers would be quite beneficial overall. An approach known as explainable artificial intelligence (XAI) was introduced to produce trustworthy predictive modeling results and applied the XAI technique known as local interpretable model-agnostic explanations (LIME) to a straightforward human interpretation. LIME investigates how extracted features are anticipated and which features are most responsible for each action. The accuracy of the extra tree classifier gives the highest accuracy of the other studied algorithms for identifying different human lower limb activities from sEMG signals.

关键词： Electromyography Sensors Feature extraction Legged locomotion Muscles Hardware Wearable sensors Electromyography (EMG) signal acquisition system explainable artificial intelligence (XAI) local interpretable model-agnostic explanations (LIME) lower limb activity recognition machine learning (ML) signal processing

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Cancellable face recognition based on fractional-order Lorenz chaotic system and Haar wavelet fusion

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DIGITAL signal processing 2021年 116卷 103103-103103页

作者： Badr, Iman S. Radwan, Ahmed G. EL-Rabaie, El-Sayed M. Said, Lobna A. El Banby, Ghada M. El-Shafai, Walid Abd El-Samie, Fathi E. Menoufia Univ Dept Elect & Elect Commun Engn Fac Elect Engn Menoufia 32952 Egypt Cairo Univ Engg Math & Phys Dept Cairo Egypt Nile Univ Sch Engn & Appl Sci Giza Egypt Nile Univ Nanoelect Integrated Syst Ctr NISC Giza Egypt Menoufia Univ Dept Ind Elect & Control Engn Fac Elect Engn Menoufia Egypt Prince Sultan Univ Dept Comp Sci Secur Engn Lab Riyadh 11586 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh 21974 Saudi Arabia

Cancellable biometrics is the art of generating distorted or encrypted templates of original biometric templates. The evolution of cancellable biometrics is attributed to the advanced hacking technologies that can capture the original stored biometrics from databases. One of the solutions for this problem is to store cancellable biometric templates in the database rather than the original ones. This paper presents a cancellable face recognition scheme that is based on face image encryption with Fractional-Order (FO) Lorenz chaotic system. The basic idea is to generate user-specific random keys to be XORed with the red, green, and blue components of color face images. These keys are generated from the fractional order Lorenz chaotic system. In addition, some post-processing is implemented on the encrypted color components of the face images with rotation and transposition of matrices. Finally, a wavelet fusion process is applied on these encrypted and processed face image components. The reason behind the utilization of wavelet fusion is to generate a single cancellable template for each color face image. Furthermore, wavelet fusion after post-processing of encrypted color components leads to a better degree of diffusion in the encrypted face templates. Actually, the encryption with the proposed algorithm is not full encryption, but it is appropriate for cancellable biometric applications. Moreover, the proposed scheme is secure due to the power of fractional-order Lorenz chaotic system that is very sensitive to initial conditions selected by the user. In addition, the post-processing incorporated with wavelet fusion is a non-invertible process. The validation of the proposed scheme is performed with experiments on FERET, LFW, and ORL databases. Evaluation metrics including Equal Error Rate (EER), Area under the Receiver Operating Characteristic (AROC) curve are utilized in the proposed scheme. Numerical values reveal EER levels close to zero and AROC values of 100%

关键词： Cancellable biometrics FO Lorenz chaotic systems Random projection (RP) Homomorphic transform AROC

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PROJECTED IMPROVED FISTA AND APPLICATION TO image DEBLURRING

PROJECTED IMPROVED FISTA AND APPLICATION TO IMAGE DEBLURRING

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IEEE International Conference on image processing (ICIP)

作者： Pokala, Praveen Kumar Seelamantula, Chandra Sekhar Indian Inst Sci Dept Elect Engn Bangalore 12 Karnataka India

ISBN: (纸本)9781728163956

The analysis-sparse model has been shown to be efficient as compared to the synthesis-sparse model when the sparsifying transform is redundant particularly for image restoration applications. We pose the image deblurring problem as an optimization problem based on the analysis-sparse model considering the sparsifying basis to be a tight frame, more specifically, the shift-invariant discrete wavelet transform (SIDWT). We propose two algorithms, namely, projected improved fast iterative soft-thresholding algorithm (piFISTA) and projected improved fast iterative soft-thresholding algorithm beyond Nesterov's momentum (piFISTA-BN). The proposed algorithms are the analysis counterparts of the improved fast iterative soft-thresholding algorithm (iFISTA) and improved fast iterative soft-thresholding algorithm beyond Nesterov's momentum (iFISTA-BN), respectively, both of which consider the synthesis-sparse model. We demonstrate that piFISTA and piFISTA-BN significantly outperform FISTA, pFISTA, iFISTA, and iFISTA-BN considering standard objective metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM). Further, we demonstrate empirically that the proposed algorithms converge faster than the state-of-the-art techniques.

关键词： Analysis sparse model tight frames projected FISTA improved FISTA beyond Nesterov's momentum

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