The proceedings contains 24 papers from the Conference on wavelet applications in signal and image processing vi. Topics discussed include: comparison of wavelets from the point of view of their approximation error;le...
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The proceedings contains 24 papers from the Conference on wavelet applications in signal and image processing vi. Topics discussed include: comparison of wavelets from the point of view of their approximation error;least statistically dependent basis and its application to image modeling;bivariate box spline wavelets in Sobolev spaces;pseudoframes for subspaces with applications;multiscale B-spline snakes for general contour detection;multilayered image compression;warping functions in speech;and fast algorithm for directional time-scale analysis using wavelets.
Due to the irrational coefficients, the orthogonal wavelet filter banks (FBs) need a lot of resources when implemented on hardware. As a result, there is a decrease in operating speed, a significant memory requirement...
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Due to the irrational coefficients, the orthogonal wavelet filter banks (FBs) need a lot of resources when implemented on hardware. As a result, there is a decrease in operating speed, a significant memory requirement, and an increase in power consumption. This paper proposes an orthogonal low complex symmetric Daubechies-2 (4-tap) wavelet filter bank (FB) to address these problems. This is accomplished by obtaining dyadic filter coefficients and making the suggested FB symmetric by marginally modifying the perfect reconstruction (PR) criterion. By using fewer adders and shifters, the suggested wavelet FB achieves a significant reduction in dynamic power consumption without the need for multipliers. This is confirmed by implementing the suggested wavelet FB on the Zedboard ZYNQ-7000 AP-SoC (Zynq FPGA from Xilinx) field programmable gate array (FPGA). The suitability of the suggested FB is tested in medical image retrieval and image compression applications. Results from simulations demonstrate that, when compared to state-of-the-art techniques, the suggested wavelet FB performs better in terms of retrieval accuracy (ARP, ARR) for medical image retrieval and PSNR for image compression on the benchmark image datasets.
In this article, a simplified efficient 2-D discrete wavelet transform (DWT) architecture based on the lifting scheme is presented. By approximating the multipliers required for the multiplication operations in the Co...
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In this article, a simplified efficient 2-D discrete wavelet transform (DWT) architecture based on the lifting scheme is presented. By approximating the multipliers required for the multiplication operations in the Cohen-Daubechies-Feauveau (CDF) 9/7 filter, computational resources are significantly reduced. In addition, the strip-based scanning method requires minimal storage resources;for single-level 2-D DWT, there is no need for RAM resources that are dependent on image size. This characteristic makes the proposed architecture particularly well-suited for applications in wireless visual sensor networks (WVSNs). The proposed 2-D DWT and IDWT hardware implementations ensure a reconstructed quality exceeding 90.56-dB peak signal-to-noise ratio (PSNR) for various N x N images with low power consumption. Compared to existing 2-D DWT architectures, our design offers significant advantages, particularly when processing larger block sizes. The area-delay product (ADP) is reduced by at least 10.02%, the energy per image (EPI) decreases by 75.98%, and power consumption is lowered by 53.52%. Furthermore, the circuit performance in the multilevel architecture is outstanding, and the savings in required resources and energy make the proposed architecture highly suitable for applications in WVSNs.
This study focuses on classifying nine different types of fruit using several deep learning architectures, including AlexNet, ResNet-50, GoogleNet, DenseNet-201, and EfficientNet-b0, while also investigating the effec...
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This study focuses on classifying nine different types of fruit using several deep learning architectures, including AlexNet, ResNet-50, GoogleNet, DenseNet-201, and EfficientNet-b0, while also investigating the effect of wavelet Transform (WT) on the classification success rates of these models. The research begins by testing these architectures on a clean, well-curated dataset, where AlexNet, ResNet, GoogleNet, DenseNet, and EfficientNet achieve impressive success rates of 93.88%, 100%, 99.34%, 99.94%, and 99.88%, respectively. However, when the models are tested on an online dataset of fruit images sourced from the internet to reflect better real-life conditions, the success rates drop significantly to 63.10%, 76.35%,74.64%, 76.63%, and 73.07%, respectively. This drop highlights the challenges posed by real-world noise, varying image backgrounds, and inconsistencies in data quality. To address this issue, the study incorporates WT as a preprocessing step and finds that it significantly improves classification accuracy, particularly for the GoogleNet architecture. The results emphasize the importance of considering real-world complexities such as background noise and image distortion when developing classification models. Furthermore, the study demonstrates the potential of WT to increase classification performance, especially in specific architectures such as GoogleNet, which shows the most significant improvement. In addition, the effect of WT was statistically analyzed and the results were evaluated. Overall, this research contributes to a deeper understanding of the factors that affect the performance of deep learning models in fruit classification tasks and provides valuable insights into optimizing these models for more robust real-world applications.
Digital watermarking plays a vital role in safeguarding data integrity and intellectual property, particularly in medical imaging, where ensuring authenticity and preserving privacy are paramount. Embedding hidden inf...
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Digital watermarking plays a vital role in safeguarding data integrity and intellectual property, particularly in medical imaging, where ensuring authenticity and preserving privacy are paramount. Embedding hidden information within images aids authentication, copyright protection, and integrity verification. This work proposes a robust palmprint-based watermarking approach based on adaptive ACM watermarking with anon- linear equation technique to enhance patient security. The proposed method integrates local binary patterns and histograms of oriented gradients for robust feature extraction, discrete wavelet transforms for multilevel image decomposition, Arnold Cat Map for chaotic mapping, and singular value decomposition for stable watermark embedding. The contribution goals are improving security, imperceptibility, and resilience against imageprocessing attacks. Experimental results demonstrate the method's effectiveness, with a peak signal-tonoise ratio of 63.52 and a structural similarity index of 1.00, indicating high image quality retention. An equal error rate of 0.035 confirms the method's reliability in watermark detection. These findings underscore the proposed method's suitability for secure medical image watermarking applications, enhancing data integrity and patient privacy.
Intellectual disability (ID) is a neurodevelopmental disease characterized by significant intellectual and adaptive functioning impairments. Continuous monitoring and data collection are crucial for managing this cond...
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Intellectual disability (ID) is a neurodevelopmental disease characterized by significant intellectual and adaptive functioning impairments. Continuous monitoring and data collection are crucial for managing this condition. To collect patient's skin temperature data, doctors and caretakers often use thermal IR imaging, and they must securely share this information. To address privacy concerns and ensure robust encryption during data transmission, this study introduces an adaptive encryption algorithm that operates in both frequency and spatial domains. The algorithm counteracts various security threats, including plain text attacks, differential attacks, brute force attacks, and cropping attacks. The core of this research is the 8D Hyperchaotic DNA Encryption Algorithm, which enhances security by combining block scrambling with DNA coding. Security is further optimized through integrating the Integer wavelet Transform (IWT) in the frequency domain and a DNA sequence in the spatial domain. The encryption process aims to balance security and computational efficiency, ensuring reliable performance. Extensive testing and validation against multiple types of attacks show the algorithm's effectiveness and reliability in practical applications. Experimental results show that the proposed system significantly enhances security against various attacks, thus setting a higher standard for encryption in biomedical image and signalprocessing. This algorithm shows potential for broader applications beyond intellectual disability care, including secure data transmission in medical imaging, financial transactions, and confidential communications, because of its robust encryption capabilities and adaptability to different data types.
With the increasing use of telemedicine in healthcare, the security and integrity of medical images during transmission have become critical. This paper presents a novel blind watermarking scheme Local Binary Pattern-...
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With the increasing use of telemedicine in healthcare, the security and integrity of medical images during transmission have become critical. This paper presents a novel blind watermarking scheme Local Binary Pattern-Discrete wavelet Transform (LBP-DWT) for medical images based on Local Binary Patterns and the *** Transform in frequency domain. We take advantage of the LBP, which is computationally fast, to improve the watermark's resistance to the different kinds of attacks, and maintain the overall visual quality of the watermarked images. In addition, the DWT could offer a high trade-off between robustness and imperceptibility due to the multi-resolution analysis it provides. During embedding, the LL band (approximation coefficients) of the DWT is selected and divided into 3 x 3 blocks. The resulting LBP codes are then XORed with the embedding bits and hidden in the corresponding blocks using the Least Significant Bit technique. Note that the Arnold transform is used during the embedding step to scramble the watermark, which is then vectorized based on the ZigZag fashion to improve the security of the proposed scheme. To evaluate the performance of the proposed method, extensive experiments are conducted on a dataset of medical images. The watermarked images are tested against various attacks, including compression, noise addition, and cropping. The obtained results demonstrate the effectiveness of the proposed techniques.
The blurred concrete crack images are typically the result of unexpected camera motion in engineering. Consequently, deblurring and segmentation of them represents a challenging task due to the complexity of the issue...
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The blurred concrete crack images are typically the result of unexpected camera motion in engineering. Consequently, deblurring and segmentation of them represents a challenging task due to the complexity of the issue. This paper presents a novel Deep Multi-stage Network (DMNet), integrated with a multi-stage feature fusion strategy, for the purpose of deblurring crack images. The proposed model is compared to other models and found to be superior. Furthermore, a novel Discrete wavelet Transform Network (DWTNet) which integrated with a multi-stage fusion approach is proposed to improve the semantic segmentation results of restored crack images. Two crack image datasets are employed to validate the proposed models and represent the typical blurring scenarios in engineering applications. The SSIM, Dice and IoU metrics are employed to provide a quantitative assessment of the proposed DMNet and DWTNet models' capabilities in terms of image deblurring and segmentation. The values of these metrics exceed those of existing models, thereby demonstrating that the DMNet combined with the DWTNet is more effective than other state-of-the-art models. It exhibits superior performance in deblurring and segmentation of crack images, respectively.
The study of visual communication and its role in conveying information through visual elements is crucial in aiding the comprehension of complex concepts. Computer imaging involves installing an operating system (OS)...
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The study of visual communication and its role in conveying information through visual elements is crucial in aiding the comprehension of complex concepts. Computer imaging involves installing an operating system (OS), applications, and settings on new devices, with cloud-based methods streamlining the process and reducing overhead. As modern society becomes more reliant on digital technology, the complexity of information systems continues to grow, further emphasizing the importance of computer technology. This paper focuses on the integration of computer imaging technology and visual communication approaches. The proposed imageprocessing system has been widely adopted and is now considered a key component of advanced, practical research. The importance of computer graphics in enhancing the quality and impact of visual communication has been fully acknowledged. Works produced using modern graphic imageprocessing techniques have the potential to significantly influence viewers' perceptions. The study explores the impact of computer graphics and image technology on the development of visual communication, providing valuable data for innovative city applications. Additionally, the paper delves into the critical role of computer graphics and image technology in realizing various aspects of visual communication prototypes. For performance analysis, quality metrics such as Mean Square Error (MSE), Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Contrast-to-Noise Ratio (CNR) are utilized, along with reliability metrics like Accuracy, Precision, Recall, and F1-Score. The proposed approach achieves superior efficiency compared to other methods. The performance results include MSE of 13%, PSNR of 40 dB, SSIM of 0.72, CNR of 39 dB, Accuracy of 98.76%, Precision of 98.64%, Recall of 97.56%, and an F1-Score of 97.87%.
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