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.
wavelet transforms have revolutionized bio signal and medical imageprocessing by providing powerful tools for analysis and feature extraction. This article aims to demystify the applications of wavelet transforms in ...
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ISBN:
(数字)9789819713202
ISBN:
(纸本)9789819713196;9789819713202
wavelet transforms have revolutionized bio signal and medical imageprocessing by providing powerful tools for analysis and feature extraction. This article aims to demystify the applications of wavelet transforms in these domains, focusing on the DWT and WPT. These transforms allow for localized representation of signals and images in both time and frequency domains, enabling detailed analysis and relevant information extraction. In bio signalprocessing, wavelet transforms are used for denoising, feature extraction, and classification of ECG, EEG, and other bio signals. In medical imageprocessing, wavelet transforms find applications in denoising, compression, image fusion, and segmentation, leading to improved analysis and diagnosis. The article provides an overview of these applications, highlighting advantages and presenting specific examples and results. wavelet transforms have great potential in bio signal and medical imageprocessing, driving healthcare and biomedical research advancements.
We propose an optical image watermarking scheme based on orbital angular momentum(OAM)*** topological charges(TCs,l)of OAM,as multiple cryptographic sub-keys,are embedded into the host image along with the watermark *...
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We propose an optical image watermarking scheme based on orbital angular momentum(OAM)*** topological charges(TCs,l)of OAM,as multiple cryptographic sub-keys,are embedded into the host image along with the watermark ***,the Arnold transformation is employed to further enhance the security and the scrambling time(m)is also served as another cryptographic *** watermark image is embedded into the host image by using the discrete wavelet transformation(DWT)and singular value decomposition(SVD)***,the interference image is utilized to further enhance *** imperceptibility of our proposed method is analyzed by using the peak signal-to-noise ratio(PSNR)and the histogram of the watermarked host *** demonstrate robustness,a series of attack tests,including Gaussian noise,Poisson noise,salt-and-pepper noise,JPEG compression,Gaussian lowpass filtering,cropping,and rotation,are *** experimental results show that our proposed method has advanced security,imperceptibility,and robustness,making it a promising option for optical image watermarking applications.
image steganography is an extremely rich and significant exploration region that gives productive answers to some genuine and modern issues. This paper deals with secret image transmission and securing it from differe...
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image steganography is an extremely rich and significant exploration region that gives productive answers to some genuine and modern issues. This paper deals with secret image transmission and securing it from different attacks. The proposed image steganography technique uses a combination of image segmentation, pre-processing, and scrambling methos. The proposed technique begins with the segmentation of secret and cover images using Rubik's cube. The segmentation process breaks the images into six parts, then after they are stored in a segmented image dataset. In the second step, each segmented secret image is processed by the image pre-processing method. The proposed image pre-processing method helps the system select the appropriate cover image from the segmented cover image dataset, which makes the technique more robust. In the third step, before the embedding process, secret images are scrambled using the proposed scrambling method. Finally, the cover and secret images are processed by discrete wavelet transforms (DWT) and singular value decomposition (SVD) to generate the stego image. The proposed technique is tested on a variety of grayscale images, with peak signal-to-noise ratio (PSNR) values ranging from 32.27 to 30.77 (dB) and structural similarity index measure (SSIM) values ranging from 0.93 to 0.90 for different alpha values. The extracted secret image is analyzed using normalized correlation (NC) and naturalness image quality evaluator (NIQE) parameters. The NC values are above 0.99, and the NIQE values ranged from 3.3264 to 3.8468 for various extracted images. To analyze the proposed technique, measured value of entropy and an elapse time are 7.7956 and 6.489 s, respectively. Comparative studies are conducted in four main areas. The first comparative study tested the reliability of the proposed scrambling method by calculating the overall and diagonal correlation values of the secret image and scrambled secret image. The second comparative study test
In this paper, a wavelet packet transform wideband beamforming (WPTWB) is proposed. The proposed method employs wavelet packet analysis and synthesis filter banks, replacing the traditional analysis and synthesis filt...
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ISBN:
(纸本)9798350350920
In this paper, a wavelet packet transform wideband beamforming (WPTWB) is proposed. The proposed method employs wavelet packet analysis and synthesis filter banks, replacing the traditional analysis and synthesis filter banks used in subband beamforming, to achieve the decomposition and reconstruction of wideband signals. And the algorithm leverages the inherent multiresolution characteristics of the wavelet packet transform to capture both the nuanced details and broader generalizations present in broadband signals. Simulation experiments demonstrate the anti-interference ability and accuracy of the algorithm.
Reduction of noise has a considerable effect in medical imageprocessing and computer vision analysis. Medical images are affected by noise due to low radiation exposure, physiological sources and electronic hardware ...
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Reduction of noise has a considerable effect in medical imageprocessing and computer vision analysis. Medical images are affected by noise due to low radiation exposure, physiological sources and electronic hardware noise. This affects diagnosis quality and quantitative measurements. In this paper, optical coherence tomography images are de-noised through wavelet transform, and the wavelet threshold value is further optimised using genetic algorithm (GA). The optimal levels of wavelet decomposition and threshold correction are performed through GA. The efficacy of the proposed method is verified by comparing the results with other reported wavelet- and GA-based methods in terms of Peak-signal-to-Noise Ratio (PSNR) parameters. The quality of the resulting image is measured through structural similarity index measure (SSIM), correlation of coefficient (COC) and edge preservation index (EPI) parameters. The improvement of the proposed approach in terms of performance parameters PSNR, COC, SSIM and EPI is respectively 2.24%, 7.9%, 17.18% and 6.32% more than the existing GA-based method considering retinal OCT image. The results indicate that the suggested algorithm effectively suppresses the speckle noise of different noise variances, and the de-noised medical image is more suitable for clinical diagnosis.
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.
image fusion is significant in various distinct sectors of imageprocessing, from remote sensing to medical applications. In recent years, real-valued wavelet transforms have been utilized to fuse images. This approac...
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image fusion is significant in various distinct sectors of imageprocessing, from remote sensing to medical applications. In recent years, real-valued wavelet transforms have been utilized to fuse images. This approach has offered enhancements against various poor approaches;however, this task lacks the shift variance and suffers from the directionality connected with its wavelet bases. Moreover, the conventional architecture of this hard wavelet decomposition implements a very hard resolution of filters to attain an essential quarter shift in the coefficient result. The establishment of image fusion methodology is to provide data integrated from distinct images to avoid inconsistency and redundancy presented among the images. This approach is utilized to enhance the utilization, reliability, accuracy, and interpretation of the data with the development of image data transparency by creating an accurate and clear detail of the monitored target. In this research work, a transform-aided image fusion mechanism is utilized to enhance the effectiveness in a better way. With the support of this approach, good "peak signal-to-noise ratio (PSNR)" with a minimum "mean square error (MSE)" can be achieved. Therefore, this work is aimed to implement a new multi-image fusion approach by fusing the normal images. Initially, the standard normal images are manually collected for the approach. Then, the decomposition of two images in the same scene is done through "optimal non-subsampled shearlet transform (ONSST)," where the attributes of NSST are optimized with the help of recommended fitness improved puzzle optimization algorithm (FIPOA). Moreover, the high-frequency fusion is done by optimal weighted average fusion, and low-frequency fusion is carried out by filter mapping-based fusion. In the end, the inverse ONSST is taken to get the final integrated images. The experimental analysis of the recommended approach is evaluated with various performance measures. The validation sho
These days imageprocessing technology has evolved a lot. Digital media can be distorted easily due to the availability of editing tools. So, watermarking can be used to protect digital media. This article proposes th...
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These days imageprocessing technology has evolved a lot. Digital media can be distorted easily due to the availability of editing tools. So, watermarking can be used to protect digital media. This article proposes the Discrete wavelet Transform (DWT) and Singular Value Decomposition (SVD) based effective and optimal robust image watermarking technique using the differential evolution (DE) algorithm, which is invariant against rotation, scaling, and translation (RST). The watermark embedding process uses a 3-level DWT and SVD. Here, speeded-up robust features (SURF) are also used to extract feature points and match features. The RST-attacked image can be restored using these features by applying the M-estimator SAmple Consensus(M-SAC) algorithm. DE algorithm is used to estimate the optimized value of the scaling factor. The optimized scaling factor is assisted in acquiring the highest possible robustness with enhanced imperceptibility. After experimentation, results illustrate the proposed technique is invariant to the RST, and signalprocessing attacks.
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