scholarly journals Contrast Enhancement Technique using Discrete Wavelet Transform with Just Noticeable Difference Model for 3D Stereoscopic Degraded Video

2021 ◽  
Vol 10 (3) ◽  
pp. 7-13
Author(s):  
Bhagya H K ◽  
Keshaveni N
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Contrast Enhancement ◽  
High Frequency ◽  
Just Noticeable Difference ◽  
Video Frame ◽  
Discrete Wavelet ◽  
Difference Model ◽  
Pass Filter ◽  
Enhancement Technique

The Video Technologies for Medical, cultural, and social activities prefer 3D visual data rendering and processing. So 3D videos are captured by any capturing devices, like the digital cameras are not acceptable all the time due to the lack of capturing devices or indecent illumination or due to poor weather surroundings like Low light, rain, fog, mist, etc. reduces the contrast, thus the videos get degraded. 3D video contrast enhancement technique is an essential process for upgrading the quality and information content in the videos. The proposed work employs a discrete wavelet transform based enhancement technique with Jut noticeable difference model to improve the video frames and it is simple and computationally inexpensive. The application of DWT results in the Low and High-frequency sub-bands. The low-frequency components that contain the greatest amount of the information are improved using weighted threshold histogram equalization(WTHE) with the JND model algorithm while the high-frequency sub-bands are distortions and highly affected by noise. The Gaussian high pass filter is applied to each high-frequency sub-bands to remove the noise. Besides, enhancement gain control and luminance preservation are used to acquire the enhanced output video. At the end check the quality of the degraded video frame, the presented work is implemented in MATLAB 2018a and evaluated using objective parameters. Experimental results show that the proposed method can generate better and agreeable results than 2D videos.

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 A Multi-Valued Diagnostic Model Synthesis Based on Descrete Wavelet Transform

2015 ◽  
Vol 27 (1) ◽  
pp. 43-52 ◽  
Author(s):  
Henryk Borowczyk
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Real Life ◽  
Signal Decomposition ◽  
Diagnostic Model ◽  
Discrete Wavelet ◽  
Model Synthesis ◽  
Vibroacoustic Signal ◽  
Three Stages

Abstract The method of a multi-valued diagnostic model synthesis using discrete wavelet transform is presented. The method's algorithm consists of three stages: (1) - signal decomposition into low- and high frequency parts - approximations and details, (2) - approximations and details parameterization, (3) - multi-valued encoding parameters obtained in stage 2. The method is illustrated with vibroacoustic signal in real life experiment. The multi-valued diagnostic model is the final result.

MULTIVARIATE SPECTRAL ANALYSIS USING HILBERT WAVELET PAIRS

2004 ◽  
Vol 02 (04) ◽  
pp. 567-587 ◽  
Author(s):  
BRANDON WHITCHER ◽  
PETER F. CRAIGMILE
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Multivariate Time Series ◽  
Discrete Wavelet ◽  
Pass Filter ◽  
Wavelet Filters ◽  
The Hilbert Transform ◽  
High Pass

We investigate the use of Hilbert wavelet pairs (HWPs) in the non-decimated discrete wavelet transform for the time-varying spectral analysis of multivariate time series. HWPs consist of two high-pass and two low-pass compactly supported filters, such that one high-pass filter is the Hilbert transform (approximately) of the other. Thus, common quantities in the spectral analysis of time series (e.g., power spectrum, coherence, phase) may be estimated in both time and frequency. Compact support of the wavelet filters ensures that the frequency axis will be partitioned dyadically as with the usual discrete wavelet transform. The proposed methodology is used to analyze a bivariate time series of zonal (u) and meridional (v) winds over Truk Island.

Pendeteksian Misalignment Menggunakan Multi Level Transformasi Wavelet Haar dan Coiflet pada Motor Induksi

2020 ◽  
Vol 1 (1) ◽  
pp. 1-6
Author(s):  
P.P.S Saputra
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Induction Motor ◽  
High Frequency ◽  
High Efficiency ◽  
Vibration Signal ◽  
Normal Operation ◽  
Discrete Wavelet ◽  
High Frequency Signal ◽  
Motor Operation

Currently induction motors are widely used in industry due to strong construction, high efficiency, and cheap maintenance. Machine maintenance is needed to prolong the life of the induction motor. As studied, bearing faults may account for 42% -50% of all motor failures. In general it is due to manufacturing faults, lack of lubrication, and installation errors. Misalignment of motor is one of the installation errors. This paper is concerned to simulation of discrete wavelet transform for identifying misalignment in induction motor. Modelling of motor operation is introduced in this paper as normal operation and two variations of misalignment. For this task, haar and coiflet discrete wavelet transform in first level until fifth level is used to extract vibration signal of motor into high frequency of signal. Then, energy signal and other signal extraction gotten from high frequency signal is evaluated to analysis condition of motor. The results show that haar discrete wavelet transform at thirth level can identify normal motor  and misalignment motor conditions well

scholarly journals Assessing the Contribution of the Oscillatory Potentials to the Genesis of the Photopic ERG with the Discrete Wavelet Transform

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Mathieu Gauvin ◽  
Allison L. Dorfman ◽  
Nataly Trang ◽  
Mercedes Gauthier ◽  
John M. Little ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Normal Subjects ◽  
Oscillatory Potentials ◽  
Discrete Wavelet ◽  
Wide Range ◽  
Frequency Components ◽  
Photopic Erg

The electroretinogram (ERG) is composed of slow (i.e., a-, b-waves) and fast (i.e., oscillatory potentials: OPs) components. OPs have been shown to be preferably affected in some diseases (such as diabetic retinopathy), while the a- and b-waves remain relatively intact. The purpose of this study was to determine the contribution of OPs to the building of the ERG and to examine whether a signal mostly composed of OPs could also exist. DWT analyses were performed on photopic ERGs (flash intensities: −2.23 to 2.64 log cd·s·m−2in 21 steps) obtained from normal subjects (n=40) and patients (n=21) affected with a retinopathy. In controls, the %OP value (i.e., OPs energy/ERG energy) is stimulus- and amplitude-independent (range: 56.6–61.6%; CV = 6.3%). In contrast, the %OPs measured from the ERGs of our patients varied significantly more (range: 35.4%–89.2%;p<0.05) depending on the pathology, some presenting with ERGs that are almost solely composed of OPs. In conclusion, patients may present with a wide range of %OP values. Findings herein also support the hypothesis that, in certain conditions, the photopic ERG can be mostly composed of high-frequency components.

scholarly journals Technical Vision System for in-Room Access Control

2021 ◽  
Vol 4 (4) ◽  
Author(s):  
Artem Ihorovych Fironov ◽  
Vitaliy Viktorovych Levchenko
Keyword(s):  
Face Recognition ◽  
Wavelet Transform ◽  
Standard Deviation ◽  
Access Control ◽  
Discrete Wavelet Transform ◽  
Output Signal ◽  
Discrete Wavelet ◽  
Pass Filter ◽  
Technical Vision ◽  
Speed Up

Access systems with face recognition is widely used today. They are used in many enterprises and institutes where it is necessary to control the flow of passing people.  Facially recognizable technical vision systems are important because they can be used to store specific individuals faces and use them for access control. As a result of analysis of same modern systems the variant of system there are additional functions is offered. The system consists of ESP-EYE module, with build-in wi-fi and Bluetooth modules, chip sensor camera “ OV2640” and LED display, which dasplays a notification for a person about granting or denying access, notifications are in two collors: geen and red respectively.. Also it has an emergency power supply in case of unforeseen situations. Wi-fi is used as a means of transmiting data from camera to the server. This transmition method of data transmition has several advantages over Bluetooth. It allows to the system to transfer data at a much higher speed and over a grater distance, it is also more secure, provides access to the internet and allows to control the system  remotely. All the listed advantages of this method of transmition give us a great variability in the operation and placement of the system. To recognize people system use a comparison method. It compares the person’s face with a database and, after processing it produces the result. To optimize and speed up this process, the system uses a method of image compression based on discrete wavelet transform. This method is the transmission of a signal through several filtres, usualy two. First, the signal is passed through a low-pass filter whis a pulse response g, resulting in an output signal in the form of a convolutional sum. At the same time the signal is decomposed by a high pass filter. The LPF gives an approximate shape of the output signal, and the HPF – the signal of difference or additional detail. Discrete wavelet transform in an oriented basis makes it possible to construct transformation matrices with a given number of filters ”m”, where “m” is in the general case a prime positive number. The simplest way to compare the two images is by substracting the brightness values of the two matrices and estimating the resulting matrix of differences using standard deviation. The use of standard deviation in combination with fiberboard in OB allows to speed up the process of face recognition in the system by discarding unncessary details, the absence of which minimaly harms the accuracy of the results. The advantages of this system are that it is less expensive, in compareson with existing analogs, less energy-consuming, easy to assemble and install, uses a relatively simple and at the same time quite accurate method of identidying a persons identity.

scholarly journals Invisible Medical Image Watermarking using Edge Detection And Discrete Wavelet Transform Coefficients

2019 ◽  
Vol 9 (1) ◽  
pp. 5074-5080
Keyword(s):  
Wavelet Transform ◽  
Medical Imaging ◽  
Edge Detection ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Medical Image ◽  
Image Watermarking ◽  
Discrete Wavelet ◽  
Medical Image Watermarking ◽  
Imaging Application

Protection and authentication of medical images is essential for the patient’s disease identification and diagnosis. The watermark in medical imaging application needs to be invisible and it is also required to preserve the low and high frequency features of image data which makes watermarking a difficult assignment. Within this manuscript an unseen medical image watermarking approach is projected apply edge detection in the discrete wavelet transform domain. The wavelet transform is brought into play to decay the medical picture interested in multi-frequency secondary band coefficients. The edge detection applies to high frequency wavelet group in the direction of generating the boundary coefficients used as a key. The Gaussian noise pattern is utilized as watermark as well as embedded within the edge coefficients around the edges. To add the robustness scaled dilated edge coefficient is added with the edge coefficients to generate the watermarked image. Preserving the small frequency secondary band fulfills the information requirement of the medical imaging application. At the same time as adding together the watermark during high frequency sub-bands improve the watermark invisibility. To add additional robustness the dilation is applied on the edged coefficient before being embedded with sub band coefficients. presentation of the technique is experienced on the dissimilar set of medical imagery as well as evaluation of the proposed watermarking method founds it robust not in favor of the different attacks such at the same time as filtering, turning round plus resizing. Parametric study foundation going on Mean Square Error along with Signal to Noise Ratio shows that how good method performs for invisibility.

Data Hiding Scheme for Video Watermarking Using Horizontal and Vertical Coefficients of Single Level Discrete Wavelet Transform Method

2021 ◽  
pp. 2250044
Author(s):  
J. Jerisha Liby ◽  
T. Jaya
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Similarity Index ◽  
Video Watermarking ◽  
Data Matrix ◽  
Experimental Result ◽  
Video Frame ◽  
Discrete Wavelet ◽  
Transform Method ◽  
Single Level

This paper proposes a new watermarking algorithm based on a single-level discrete wavelet transform (DWT). This method initially chooses ‘[Formula: see text]’ number of carrier frames to hide the data. After estimating the carrier frames, each frame is separated into RGB frames. Each R, G, and B frames are decomposed using a single-level DWT. The horizontal and vertical coefficients are selected to embed the watermark information since small changes in the horizontal and vertical coefficients do not highly affect the quality of the video frame. The watermark image pixels are shuffled using a predetermined key before embedding. The shuffled pixels are converted to binary, and they are grouped into three data matrices. Each data matrix is embedded in horizontal and vertical coefficients of the R, G and B frames of the video frame. After embedding the data, the watermarked video is reconstructed using the original approximation coefficients, the embed coefficients, and the original diagonal coefficients. During the extraction process, the watermark is extracted from the horizontal and vertical coefficients of the watermarked video. Experimental result reveals that the proposed method outperforms other related methods in terms of video quality and structural similarity index measurement.

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