- 标题
- 摘要
- 关键词
- 实验方案
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[IEEE 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) - Chennai (2018.3.22-2018.3.24)] 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) - Automatic Segmentation of Exudates in Retinal Images
摘要： This paper presents a new technique for segmentation of exudates in fundus images. This technique is based on Discrete Wavelet Transform (DWT) and histogram based thresholding procedure. In this work, Optic Disc (OD) is eliminated using DWT from original green component image prior segmentation of exudates. This step aids to avoid the misclassification of exudates region. Histogram based threshold calculation procedure is introduced for segmentation of bright regions in green component image. Hard exudates are obtained after masking the OD region in segmented bright regions of the green component image. This technique was evaluated on images from DIARETDB0 and DIARETDB1 databases. The average sensitivity, specificity and accuracy achieved by proposed method are 0.7890, 0.9972 and 0.9964 respectively. Comparison with existing methods offered in the literature shows that the performance of proposed approach is significant.
关键词： Optic Disc,Retinal image,Segmentation,Exudates,Discrete Wavelet Transform
更新于2025-09-23 15:22:29
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Dual tree complex wavelet transform incorporating SVD and bilateral filter for image denoising
摘要： In recent years massive production of digital images increased the need for image denoising. The effect of noise can be removed by using spatial and frequency domain approaches. Discrete Wavelet Transforms (DWT) is a frequency domain approach, which removes the noise by shrinking the wavelet coefficients using simple threshold value. Even though wavelet transform is popularly used in image processing applications, shift variance and poor directional selectivity are the two noteworthy limitations. In order to overcome these limitations, Dual Tree Complex Wavelet Transform (DTCWT) is used for perfect reconstruction of noisy image. A DTCWT incorporating Singular Value Decomposition (SVD) with Frobenius energy correcting factor and bilateral filter for image denoising using bivariate shrinkage function for thresholding the image is proposed in this paper. The denoising performance of the proposed method in terms of PSNR and it indicates that the proposed method outperforms over other existing techniques.
关键词： bilateral filter,SVD,bivariate shrinkage,thresholding technique,wavelet transform,DTCWT,image denoising
更新于2025-09-23 15:22:29
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Infrared super-resolution imaging using multi-scale saliency and deep wavelet residuals
摘要： Infrared (IR) imaging systems with low-density focal plane arrays produce images with poor spatial resolution. To address this limitation, super-resolution (SR) algorithms can be applied on IR-low resolution (LR) images. In this paper, we present a new SR technique based on the multi-scale saliency detection and the residuals learned by the deep convolutional neural network (CNN) in the wavelet domain (DWCNN). The input LR image is processed in the transformed domain by applying 2D discrete wavelet transform. It decomposes an image into its low-frequency and high-frequency subbands. The multi-scale saliency detection is used to extract small scale and large scale salient feature maps from the bicubic upscaled LR image. These maps are incorporated in the high-frequency subbands of the LR image. Furthermore, the low-frequency and high-frequency subands are re?ned using the residuals learned by the DWCNN in training phase. The proposed algorithm is compared with the conventional and state-of-the-art SR methods. Results indicate that our method yields good reconstruction quality with high peak signal to ratio, structural similarity and low blur indices. Besides, our method requires less computational time.
关键词： Infrared imaging,Convolutional neural network,Discrete wavelet transform,Multi-scale saliency,Super-resolution
更新于2025-09-23 15:22:29
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[IEEE 2018 26th European Signal Processing Conference (EUSIPCO) - Rome (2018.9.3-2018.9.7)] 2018 26th European Signal Processing Conference (EUSIPCO) - Wavelet-Based Classification of Transient Signals for Gravitational Wave Detectors
摘要： The detection of gravitational waves opened a new window on the cosmos. The Advanced LIGO and Advanced Virgo interferometers will probe a larger volume of Universe and discover new gravitational wave emitters. Characterizing these detectors is of primary importance in order to recognize the main sources of noise and optimize the sensitivity of the searches. Glitches are transient noise events that can impact the data quality of the interferometers and their classification is an important task for detector characterization. In this paper we present a classification method for short transient signals based on a Wavelet decomposition and de-noising and a classification of the extracted features based on XGBoost algorithm. Although the results show the accuracy is lower than that obtained with the use of deep learning, this method which extracts features while detecting signals in real time, can be configured as a fast classification system.
关键词： machine learning classification,signal processing,wavelet decomposition
更新于2025-09-23 15:22:29
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Image-segmentation algorithm based on wavelet and data-driven neutrosophic fuzzy clustering
摘要： Aim to that Neutrosophic C-mean clustering segmentation does not consider the membership distribution of every sample point to different classes. Herein, an image-segmentation algorithm based on wavelet and data-driven neutrosophic fuzzy clustering is proposed. When the maximum membership value of a sample point is far greater than other membership values, the centre of the class with the maximum membership value is taken as the centre of the fuzzy class. Otherwise, the average value of the centre of the two classes with the highest and second-highest membership values is used as the centre of the fuzzy class. In the preprocessing stage, wavelet technology is used to remove noise from the processed image, and the improved Bayesian algorithm is employed to calculate the filter threshold. The experiment results for synthetic and natural images show that the proposed method is more accurate and effective than the existing methods.
关键词： Image segmentation,wavelet transformation,neutrosophic fuzzy clustering
更新于2025-09-23 15:22:29
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Impact Localization System for Composite Barrel Structure using Fiber Bragg Grating Sensors
摘要： Composite barrel structures are widely used in manufacturing of aircrafts and spacecrafts, the localization of impact on composite barrel structures are essential for structure health monitoring and safety assurance. In this paper, an LVI localization system was established on a composite barrel structure with fiber Bragg grating sensors, by analyzing the relationship between the wavelet packet energy spectrum of LVI response signals monitored by FBG sensors and corresponding impact locations on composite barrel structure, the zeroth node’s energy was found to be sensitive to LVI location, and an impact localization method for composite barrel structure which use the zeroth node’s energies as LVI feature values to predict the LVI locations by means of support vector regression (SVR) was proposed. The performance of the zeroth node’s energy based localization method were compared with localization methods that based on the energy of the fourth node which covers the natural frequency of the composite barrel structure or the total energy of frequency domain. The proposed localization method based on the zeroth node’s energy and SVR demonstrates an effective and practical means for localization of LVI on composite barrel structure with low sampling rate fiber bragg grating sensors and small computation.
关键词： Composite barrel structure,Wavelet analysis,Fiber Bragg gratings.,Impact localization
更新于2025-09-23 15:22:29
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Speckle Noise Reduction of Medical Imaging via Logistic Density in Redundant Wavelet Domain
摘要： In the digital world, artificial intelligence tools and machine learning algorithms are widely applied in analysis of medical images for identifying diseases and make diagnoses; for example, to make recognition and classification. Speckle noises affect all medical imaging systems. Therefore, reduction in corrupting speckle noises is very important, since it deteriorates the quality of the medical images and makes tasks such as recognition and classification difficult. Most existing denoising algorithms have been developed for the additive white Gaussian noise (AWGN). However, AWGN is not a speckle noise. Therefore, this work presents a novel speckle noise removal algorithm within the framework of Bayesian estimation and wavelet analysis. This research focuses on noise reduction by the Bayesian with wavelet-based method because it provides good efficiency in noise reduction and spends short time in processing. The subband decomposition of a logarithmically transformed image is best described by a family of heavy-tailed densities such as Logistic distribution. Then, this research proposes the maximum a posteriori (MAP) estimator assuming Logistic random vectors for each parent-child wavelet coefficient of noise-free log-transformed data and log-normal density for speckle noises. Moreover, a redundant wavelet transform, i.e., the cycle-spinning method, is applied in our proposed methods. In our experiments, our proposed methods give promising denoising results.
关键词： wavelet transforms.,Bayesian estimation,speckle noise,non-Gaussian model
更新于2025-09-23 15:22:29
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Achieving Super-Resolution Remote Sensing Images via the Wavelet Transform Combined With the Recursive Res-Net
摘要： Deep learning (DL) has been successfully applied to single image super-resolution (SISR), which aims at reconstructing a high-resolution (HR) image from its low-resolution (LR) counterpart. Different from most current DL-based methods, which perform reconstruction in the spatial domain, we use a scheme based in the frequency domain to reconstruct the HR image at various frequency bands. Further, we propose a method that incorporates the wavelet transform (WT) and the recursive Res-Net. The WT is applied to the LR image to divide it into various frequency components. Then, an elaborately designed network with recursive residual blocks is used to predict high-frequency components. Finally, the reconstructed image is obtained via the inverse WT. This paper has three main contributions: 1) an SISR scheme based on the frequency domain is proposed under a DL framework to fully exploit the potential to depict images at different frequency bands; 2) recursive block and residual learning in global and local manners are adopted to ease the training of the deep network, and the batch normalization layer is removed to increase the flexibility of the network, save memory, and promote speed; and 3) the low-frequency wavelet component is replaced by an LR image with more details to further improve performance. To validate the effectiveness of the proposed method, extensive experiments are performed using the NWPU-RESISC45 data set, and the results demonstrate that the proposed method outperforms state-of-the-art methods in terms of both objective evaluation and subjective perspective.
关键词： residual learning,wavelet transform (WT),remote sensing image,super resolution,Recursive network
更新于2025-09-23 15:22:29
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Evaluating Feature Extractors and Dimension Reduction Methods for Near Infrared Face Recognition Systems
摘要： This study evaluates the performance of global and local feature extractors as well as dimension reduction methods in NIR domain. Zernike moments (ZMs), Independent Component Analysis (ICA), Radon Transform + Discrete Cosine Transform (RDCT), Radon Transform + Discrete Wavelet Transform (RDWT) are employed as global feature extractors and Local Binary Pattern (LBP), Gabor Wavelets (GW), Discrete Wavelet Transform (DWT) and Undecimated Discrete Wavelet Transform (UDWT) are used as local feature extractors. For evaluation of dimension reduction methods Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPDA), Linear Discriminant Analysis + Principal Component Analysis (Fisherface), Kernel Fisher Discriminant Analysis (KFD) and Spectral Regression Discriminant Analysis (SRDA) are used. Experiments conducted on CASIA NIR database and PolyU-NIRFD database indicate that ZMs as a global feature extractor, UDWT as a local feature extractor and SRDA as a dimension reduction method have superior overall performance compared to some other methods in the presence of facial expressions, eyeglasses, head rotation, image noise and misalignments.
关键词： comparative study,undecimated discrete wavelet transform,Face recognition,near infrared,Zernike moments
更新于2025-09-23 15:22:29
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An improved infrared image processing method based on adaptive threshold denoising
摘要： This paper combines the image adaptive threshold denoising algorithm and performs double threshold mapping processing to the infrared image, which effectively reduces the influence of these phenomena to the infrared image and improves the quality of the image. In this paper, the infrared image denoising technology is studied, and an infrared image denoising method based on the wavelet coefficient threshold processing is proposed. This method is based on the noise distribution characteristics of infrared images, the multiplicative noise in the infrared image is transformed into an additive noise, and the wavelet transform coefficient of the transformed infrared image is processed to denoise the image. On this basis, the advantages and disadvantages of the soft and hard threshold functions are deeply analyzed, and an adaptive threshold function with adjustable parameter is constructed. At the same time, in order to suppress the Gibbs visual distortion caused by the absence of translation invariance of the orthogonal wavelet transform, the two-input wavelet transform with translation invariance is introduced, and a double threshold mapping infrared image processing method based on the adaptive threshold denoising algorithm based on the two-input wavelet transform is formed. Simulation results show that the method proposed in this paper has a better suppression of noise, maintains the integrity of image details, and improves the image quality to a certain extent.
关键词： Threshold function,Double threshold mapping,Image denoising,Binary wavelet transform,Infrared image
更新于2025-09-23 15:22:29