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.
This paper comprehensively overviews image and signalprocessing, including their fundamentals, advanced techniques, and applications. imageprocessing involves analyzing and manipulating digital images, while signal ...
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In digital histopathology, color standardization, known as stain normalization, is widely used in computer-aided diagnosis (CAD) systems. This study details the adaptation and implementation of the wavelet Knowledge D...
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ISBN:
(纸本)9798350388978;9798350388961
In digital histopathology, color standardization, known as stain normalization, is widely used in computer-aided diagnosis (CAD) systems. This study details the adaptation and implementation of the wavelet Knowledge Distillation (WKD) method to CAD systems. The proposed method focuses on knowledge transfer between the teacher and student models within a specially designed Pix2Pix Generative Adversarial Network (GAN) for stain normalization in histopathology images. The student model, guided by the knowledge transferred by the teacher model using wavelet-based feature extraction, significantly improves the accuracy of stain normalization, which is crucial for preserving histological details and image quality. The WKD method has demonstrated high performance on the publicly available paired MITOS-ATYPIA dataset, outperforming state-of-the-art methods. Using the same settings, the Teacher model achieved a PSNR of 25.559, SSIM of 0.934, and RMSE of 7.270. Additionally, the student model used in the method yielded better results in the Frechet Inception *** (FID) metric compared to the teacher and baseline models.
imageprocessing related to noise suppression may entail partial distortion of useful visual information caused by filtering. Preservation of contours and texture details in the process of image denoising is one of th...
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Most of the existing waveletimageprocessing techniques are carried out in the form of single-scale reconstruction and multiple iterations. However, processing high-quality fMRI data presents problems such as mixed n...
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image de-noising is an essential field in imageprocessing, encompassing a wide range of applications. This is pre-processing task in which unwanted noise signals are removed using different techniques. Noise are unwa...
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wavelet neural networks are a unique combination of wavelet analysis and neural network architectures, thus providing the benefits of advanced signalprocessing. Unlike traditional methods, WNNs offer a multi-resoluti...
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wavelet analysis of one-dimensional signals has proven effective in deciphering the electrocardiogram (ECG). Promising results have already been obtained from their analysis. In particular, it has been shown that anom...
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ISBN:
(纸本)9783031538261;9783031538278
wavelet analysis of one-dimensional signals has proven effective in deciphering the electrocardiogram (ECG). Promising results have already been obtained from their analysis. In particular, it has been shown that anomalous effects in the ECG are mainly manifested on much larger scales (low frequencies), while normal structures are characterized by relatively small scales (high frequencies). Denoising is one of the urgent problems of digital processing of biomedical signals and tomographic images. wavelet methods are relatively new and are a method of denoising usingwavelet functions. wavelets allowfor the analysis of various types of signals and effective noise removal, so it is of particular interest to study their potential to improve image quality. It is very convenient to use DWT (Discrete wavelet Transform) in digital imageprocessing, because it provides deep insight into the main spatial and frequency features. wavelets provide excellent time-frequency localization, meaning they can capture both transient and stationary features in signals and images. This localization capability is especially valuable in medical applications where signals may contain abrupt changes or irregular patterns. In this paper, we will discuss the method of biomedical signal restoration and denoising in tomographic images using different wavelet functions such as Haar wavelet, Symlet, Meyer wavelet, Daubechies wavelet.
images captured in low light conditions usually suffer from poor visibility, a high amount of noise, and little information stored in the dark image, which has a negative impact on subsequent processing for outdoor co...
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ISBN:
(纸本)9798350374292;9798350374285
images captured in low light conditions usually suffer from poor visibility, a high amount of noise, and little information stored in the dark image, which has a negative impact on subsequent processing for outdoor computer vision applications. Presently, numerous deep learning based methods achieved superior performance with multi-exposure paired training data or additional information. However, obtaining multi-exposure data samples is a tedious task in real-time scenarios. To mitigate this challenge, we propose a zero reference based learnable wavelet approach without multi-exposure paired training data requirement for low-light image enhancement. Our proposed approach generates the low light image and learns to project an image into noise free similar looking image, then we enhance the image using retinex theory. Further, we have proposed learnable wavelet block to remove the hidden noise amplified while enhancement. We introduce Gaussian-based supervision to improve the smoothness of the image. Extensive experimental analysis on synthetic as well as real-world images, along with thorough ablation study demonstrate the effectiveness of our proposed method over the existing state-of-the-art methods for low-light image enhancement. The code is provided at https://***/vision-lab-sggsiet/Zero-Reference-based-Low-light-Enhancement-with-wavelet-Optimization.
wavelet-based totally records compression is a form of information compression used to method far flung sensing and picture processing. This technique makes use of wavelets, that are mathematical features that divide ...
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