Research of Wireless Transmission Based on Pulse Signal

2013 ◽  
Vol 791-793 ◽  
pp. 1965-1969
Author(s):  
Kai Sheng Zhang ◽  
Hui Liu ◽  
Wei Tang
Keyword(s):  
Wavelet Transform ◽  
Personal Computer ◽  
High Frequency ◽  
Wavelet Decomposition ◽  
Low Frequency ◽  
Pulse Signal ◽  
Wireless Transmission ◽  
Baseline Correction ◽  
Pulse Sensor ◽  
Computer Analyses

The system uses dual MCU design to achieve the acquisition and transmission of pulse signal without affecting the users sleeping process. The pulse signal is collected by infrared pulse sensor, and is wireless transferred to the personal computer by JF24C after the process of amplification, baseline correction and so on. Then personal computer analyses the signal using the method of wavelet transform algorithm. With wavelet decomposition, pulse signal can be decomposed into different scales of wavelet coefficients and recomposed according to the low-frequency coefficient of the bottom and high-frequency coefficients of various layers. During the process of wave recomposition , the coefficients related to noise can be rejected while the valuable information in the pulse signal maintained, and all the processes achieve the restoration of pulse signal. What is more, after simulating the wavelet transform algorithm using MATLAB, the consequences of this simulation indicate that multiple interferences can be effectively suppressed while keeping the pulse signal undistorted and it sustains the relevant characteristics of pulse signal.

Medical Image Condensation Based on Wavelet Transform

2015 ◽  
Vol 719-720 ◽  
pp. 978-981
Author(s):  
Xu Cao ◽  
Hua Xun Zhang
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Medical Image ◽  
Wavelet Decomposition ◽  
Ultrasonic Imaging ◽  
Three Dimensional ◽  
Low Frequency ◽  
Frequency Region ◽  
Vacuum Region ◽  
Image Use

This paper based on the peculiarity of wavelet transform that its transform only in vacuum region and frequency region, decompose image use the theory of wavelet, obtain a series sub-image of different resolution ratio. The value of higeresolution ratio sub-image is all verge on 0, the phenomenon is more obviously in high frequency, so that, the mainly proportion is low frequency to a image. Use wavelet decomposition get rid of the high frequency, only reservation low frequency, to realize the aim of condensation image. Through the simulation of contradistinctive image of cerebra framework remotion between three-dimensional ultrasonic imaging in course of OPS and MIR preceding OPS validated the feasibility by Matlab.

scholarly journals Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm

2014 ◽  
Vol 14 (2) ◽  
pp. 102-108 ◽  
Author(s):  
Yong Yang ◽  
Shuying Huang ◽  
Junfeng Gao ◽  
Zhongsheng Qian
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Discrete Wavelet ◽  
Evaluation Indexes ◽  
Fused Image ◽  
Focus Image

Abstract In this paper, by considering the main objective of multi-focus image fusion and the physical meaning of wavelet coefficients, a discrete wavelet transform (DWT) based fusion technique with a novel coefficients selection algorithm is presented. After the source images are decomposed by DWT, two different window-based fusion rules are separately employed to combine the low frequency and high frequency coefficients. In the method, the coefficients in the low frequency domain with maximum sharpness focus measure are selected as coefficients of the fused image, and a maximum neighboring energy based fusion scheme is proposed to select high frequency sub-bands coefficients. In order to guarantee the homogeneity of the resultant fused image, a consistency verification procedure is applied to the combined coefficients. The performance assessment of the proposed method was conducted in both synthetic and real multi-focus images. Experimental results demonstrate that the proposed method can achieve better visual quality and objective evaluation indexes than several existing fusion methods, thus being an effective multi-focus image fusion method.

scholarly journals An Image Denoising Method with Enhancement of the Directional Features Based on Wavelet and SVD Transforms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Min Wang ◽  
Zhen Li ◽  
Xiangjun Duan ◽  
Wei Li
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Image Denoising ◽  
High Frequency ◽  
Low Frequency ◽  
Singular Value ◽  
Denoising Method ◽  
Diagonal Part ◽  
Value Decomposition ◽  
Image Part

This paper proposes an image denoising method, using the wavelet transform and the singular value decomposition (SVD), with the enhancement of the directional features. First, use the single-level discrete 2D wavelet transform to decompose the noised image into the low-frequency image part and the high-frequency parts (the horizontal, vertical, and diagonal parts), with the edge extracted and retained to avoid edge loss. Then, use the SVD to filter the noise of the high-frequency parts with image rotations and the enhancement of the directional features: to filter the diagonal part, one needs first to rotate it 45 degrees and rotate it back after filtering. Finally, reconstruct the image from the low-frequency part and the filtered high-frequency parts by the inverse wavelet transform to get the final denoising image. Experiments show the effectiveness of this method, compared with relevant methods.

DEPSO With DTCWT Algorithm for Multimodal Medical Image Fusion

2021 ◽  
Vol 12 (4) ◽  
pp. 78-97
Author(s):  
Hassiba Talbi ◽  
Mohamed-Khireddine Kholladi
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Low Frequency ◽  
Weighted Average ◽  
Optimization Method ◽  
Absolute Maximum ◽  
Complex Wavelet Transform ◽  
Medical Image Fusion

In this paper, the authors propose an algorithm of hybrid particle swarm with differential evolution (DE) operator, termed DEPSO, with the help of a multi-resolution transform named dual tree complex wavelet transform (DTCWT) to solve the problem of multimodal medical image fusion. This hybridizing approach aims to combine algorithms in a judicious manner, where the resulting algorithm will contain the positive features of these different algorithms. This new algorithm decomposes the source images into high-frequency and low-frequency coefficients by the DTCWT, then adopts the absolute maximum method to fuse high-frequency coefficients; the low-frequency coefficients are fused by a weighted average method while the weights are estimated and enhanced by an optimization method to gain optimal results. The authors demonstrate by the experiments that this algorithm, besides its simplicity, provides a robust and efficient way to fuse multimodal medical images compared to existing wavelet transform-based image fusion algorithms.

A New Infrared Image Magnification and Enhancement Method Based on Wavelet Transform

2012 ◽  
Vol 198-199 ◽  
pp. 238-243 ◽  
Author(s):  
Wen Sheng Guo ◽  
Feng Chen ◽  
Zhao You Sun ◽  
Xi Jun Wang
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Infrared Image ◽  
Low Frequency ◽  
High Energy ◽  
Low Energy ◽  
Image Magnification ◽  
Edge Profile ◽  
Enhancement Method ◽  
Image Edge

The traditional image magnify method usually have some defects on details. This paper gives a new infrared image magnification and enhancement method which is based on wavelet reconstruction and gradation segment. In this method, first of all, make wavelet transform on the image, get the high-frequency coefficient. Apply the Newton differential algorithm enhance the high-frequency coefficient as the high-frequency part of the magnified image, treat the original image as the low-frequency part , make the wavelet reconstruction ,then get the magnified image. To enhance the magnified image, according to the double gray threshold, segment the image into high gray segment corresponding to target, low gray segment corresponding to background, and middle gray segment corresponding to transition sector. Then, make linear extension to them respectively; the result is the magnified image. Experiments indicate, this method is effective on distinguishing high-energy target from low-energy target (the low-energy target is the primary one) and displaying the details of image(edge profile of the bomb).

Infrared and visible image fusion using dual-tree complex wavelet transform and convolutional sparse representation

2020 ◽  
Vol 39 (3) ◽  
pp. 4617-4629
Author(s):  
Chengrui Gao ◽  
Feiqiang Liu ◽  
Hua Yan
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Sparse Representation ◽  
High Frequency ◽  
Low Frequency ◽  
Infrared Images ◽  
Complex Wavelet Transform ◽  
Visible Image ◽  
Visible Images ◽  
Complex Wavelet

Infrared and visible image fusion refers to the technology that merges the visual details of visible images and thermal feature information of infrared images; it has been extensively adopted in numerous image processing fields. In this study, a dual-tree complex wavelet transform (DTCWT) and convolutional sparse representation (CSR)-based image fusion method was proposed. In the proposed method, the infrared images and visible images were first decomposed by dual-tree complex wavelet transform to characterize their high-frequency bands and low-frequency band. Subsequently, the high-frequency bands were enhanced by guided filtering (GF), while the low-frequency band was merged through convolutional sparse representation and choose-max strategy. Lastly, the fused images were reconstructed by inverse DTCWT. In the experiment, the objective and subjective comparisons with other typical methods proved the advantage of the proposed method. To be specific, the results achieved using the proposed method were more consistent with the human vision system and contained more texture detail information.

scholarly journals Image Denoising Using Singular Value Difference in the Wavelet Domain

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Min Wang ◽  
Wei Yan ◽  
Shudao Zhou
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
High Frequency ◽  
Low Frequency ◽  
Singular Values ◽  
Singular Value ◽  
Wavelet Domain ◽  
Noisy Image ◽  
Denoising Method ◽  
Diagonal Part

Singular value (SV) difference is the difference in the singular values between a noisy image and the original image; it varies regularly with noise intensity. This paper proposes an image denoising method using the singular value difference in the wavelet domain. First, the SV difference model is generated for different noise variances in the three directions of the wavelet transform and the noise variance of a new image is used to make the calculation by the diagonal part. Next, the single-level discrete 2-D wavelet transform is used to decompose each noisy image into its low-frequency and high-frequency parts. Then, singular value decomposition (SVD) is used to obtain the SVs of the three high-frequency parts. Finally, the three denoised high-frequency parts are reconstructed by SVD from the SV difference, and the final denoised image is obtained using the inverse wavelet transform. Experiments show the effectiveness of this method compared with relevant existing methods.

An Image Fusion Algorithm Based on Redundant Wavelet Transform

2014 ◽  
Vol 687-691 ◽  
pp. 3656-3661
Author(s):  
Min Fen Shen ◽  
Zhi Fei Su ◽  
Jin Yao Yang ◽  
Li Sha Sun
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Source Image ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Imaging Characteristics ◽  
Focus Image ◽  
Redundant Wavelet Transform

Because of the limit of the optical lens’s depth, the objects of different distance usually cannot be at the same focus in the same picture, but multi-focus image fusion can obtain fusion image with all goals clear, improving the utilization rate of the image information ,which is helpful to further computer processing. According to the imaging characteristics of multi-focus image, a multi-focus image fusion algorithm based on redundant wavelet transform is proposed in this paper. For different frequency domain of redundant wavelet decomposition, the selection principle of high-frequency coefficients and low-frequency coefficients is respectively discussed .The fusion rule is that,the selection of low frequency coefficient is based on the local area energy, and the high frequency coefficient is based on local variance combining with matching threshold. As can be seen from the simulation results, the method given in the paper is a good way to retain more useful information from the source image , getting a fusion image with all goals clear.

Wavelet Transform with a Novel Integration Technique for Image Fusion

2011 ◽  
Vol 204-210 ◽  
pp. 1419-1422 ◽  
Author(s):  
Yong Yang
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Experimental Results ◽  
Integration Technique ◽  
Frequency Bands ◽  
Fusion Methods ◽  
Fusion Scheme ◽  
Measures Of Performance

Image fusion is to combine several different source images to form a new image by using a certain method. Recent studies show that among a variety of image fusion algorithms, the wavelet-based method is more effective. In the wavelet-based method, the key technique is the fusion scheme, which can decide the final fused result. This paper presents a novel fusion scheme that integrates the wavelet decomposed coefficients in a quite separate way when fusing images. The method is formed by considering the different physical meanings of the coefficients in both the low frequency and high frequency bands. The fused results were compared with several existing fusion methods and evaluated by three measures of performance. The experimental results can demonstrate that the proposed method can achieve better performance than conventional image fusion methods.

Stereoscopic Images Enhancement Based on Edge Sharpening of Wavelet Coefficients

2014 ◽  
Vol 511-512 ◽  
pp. 490-494 ◽  
Author(s):  
Yi Min Qiu ◽  
Shi Hong Chen ◽  
Yi Zhou ◽  
Xin Hai Liu
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Subjective Assessment ◽  
Wavelet Coefficients ◽  
Stereoscopic Images ◽  
Multi Scale ◽  
Image Edge ◽  
Frequency Approximation ◽  
Edge Sharpening

This paper proposed a new image enhancement algorithm based on edge sharpening of wavelet coefficients for stereoscopic images. Our scheme uses the multi-scale characteristic of wavelet transform, decomposes the original image into low frequency approximation sub-graph and several high frequency direction. Under the multi-scale, the low frequency approximation sub-graph is processed by edge sharpening method. Then the low frequency sub-graph decomposes in multi-scale again. At last, the low frequency approximation graph after four layers decompose sharpening and the high frequency approximation of the decomposed sub-graph will be refactored to get the new image. Experimental results show that whether PSNR or visual effect, or the subjective assessment of the DMOS value, the proposed method has better enhanced performance than the conventional edge sharpening and wavelet transform. And it has good image edge enhancement, details protection. Meanwhile, the proposed algorithm has the same computational complexity with wavelet transform.

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