scholarly journals Algorithm Design for Edge Detection of High-Speed Moving Target Image under Noisy Environment

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 343
Author(s):  
Fangfang Han ◽  
Bin Liu ◽  
Junchao Zhu ◽  
Baofeng Zhang
Keyword(s):  
Wavelet Transform ◽  
Edge Detection ◽  
High Frequency ◽  
High Speed ◽  
Moving Target ◽  
Noisy Image ◽  
Wavelet Coefficients ◽  
Edge Information ◽  
Wavelet Filters ◽  
Transform Coefficients

For some measurement and detection applications based on video (sequence images), if the exposure time of camera is not suitable with the motion speed of the photographed target, fuzzy edges will be produced in the image, and some poor lighting condition will aggravate this edge blur phenomena. Especially, the existence of noise in industrial field environment makes the extraction of fuzzy edges become a more difficult problem when analyzing the posture of a high-speed moving target. Because noise and edge are always both the kind of high-frequency information, it is difficult to make trade-offs only by frequency bands. In this paper, a noise-tolerant edge detection method based on the correlation relationship between layers of wavelet transform coefficients is proposed. The goal of the paper is not to recover a clean image from a noisy observation, but to make a trade-off judgment for noise and edge signal directly according to the characteristics of wavelet transform coefficients, to realize the extraction of edge information from a noisy image directly. According to the wavelet coefficients tree and the Lipschitz exponent property of noise, the idea of neural network activation function is adopted to design the activation judgment method of wavelet coefficients. Then the significant wavelet coefficients can be retained. At the same time, the non-significant coefficients were removed according to the method of judgment of isolated coefficients. On the other hand, based on the design of Daubechies orthogonal compactly-supported wavelet filter, rational coefficients wavelet filters can be designed by increasing free variables. By reducing the vanishing moments of wavelet filters, more high-frequency information can be retained in the wavelet transform fields, which is benefit to the application of edge detection. For a noisy image of high-speed moving targets with fuzzy edges, by using the length 8-4 rational coefficients biorthogonal wavelet filters and the algorithm proposed in this paper, edge information could be detected clearly. Results of multiple groups of comparative experiments have shown that the edge detection effect of the proposed algorithm in this paper has the obvious superiority.

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.

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.

scholarly journals Digital Watermarking using Multiscale Ridgelet Transform

2013 ◽  
pp. 162-167
Author(s):  
Manmit Kaur ◽  
H. P. Sinha
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Edge Detection ◽  
Discrete Cosine Transform ◽  
Digital Watermarking ◽  
High Frequency ◽  
Digital Images ◽  
Experimental Results ◽  
Ridgelet Transform ◽  
Frequency Bands

The multi-resolution watermarking method for digital images proposed in this work. The multiscale ridgelet coefficients of low and high frequency bands of the watermark is embedded to the most significant coefficients at low and high frequency bands of the multiscale ridgelet of an host image, respectively. A multi-resolution nature of multiscale ridgelet transform is exploiting in the process of edge detection. Experimental results of the proposed watermarking method are compared with the previously available watermarking algorithm wavelet transform. Moreover, the proposed watermarking method also tested on images attached by Discrete Cosine Transform (DCT) and wavelet based lossy image compression techniques.

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.

An Adaptive Rayleigh-Laplacian Based MAP Estimation Technique for Despeckling SAR Images using Stationary Wavelet Transform

2013 ◽  
Vol 4 (4) ◽  
pp. 88-102
Author(s):  
Amlan Jyoti Das ◽  
Anjan Kumar Talukdar ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Parameter Estimation ◽  
Wavelet Transform ◽  
Speckle Noise ◽  
Posteriori Probability ◽  
Wavelet Coefficients ◽  
Stationary Wavelet Transform ◽  
Sar Images ◽  
Edge Information ◽  
A Posteriori Probability ◽  
Map Estimator

Removal of speckle noise from Synthetic Aperture Radar (SAR) images is an important step before performing any image processing operations on these images. This paper presents a novel Stationary Wavelet Transform (SWT) based technique for the purpose of removing the speckle noise from the SAR returns. Maximum a posteriori probability (MAP) condition which uses a prior knowledge is used to estimate the noise free wavelet coefficients. The proposed MAP estimator is designed for this purpose which uses Rayleigh distribution for modeling the speckle noise and Laplacian distribution for modeling the statistics of the noise free wavelet coefficients. The parameters required for MAP estimator is determined by technique used for parameter estimation after SWT. Moreover an Laplacian – Gaussian based MAP estimator is also applied and the parameter estimation is done using the same method used for the proposed algorithm. For the purpose of enhancing the visual quality and to restore more edge information, a wavelet based resolution enhancement technique is also used after applying the Inverse stationary Wavelet Transform (ISWT), using interpolation technique. The experimental results show that the proposed despeckling algorithm efficiently removes speckle noise from the SAR images and restores the edge information as well.

scholarly journals Granulometric analysis based on the energy of Wavelet Transform coefficients

2010 ◽  
Vol 63 (2) ◽  
pp. 347-354 ◽  
Author(s):  
Elisângela Fátima Oliveira ◽  
Andrea Gomes Campos Bianchi ◽  
Luiz de Siqueira Martins-Filho ◽  
Romuel Figueiredo Machado
Keyword(s):  
Image Analysis ◽  
Wavelet Transform ◽  
Size Distribution ◽  
Wavelet Coefficients ◽  
Granulometric Analysis ◽  
Transform Coefficients ◽  
Simulated Images

A methodology for granulometric image analysis based on modeling the spatial energy of frequencies, using the Wavelet transform is presented in this article. A brief review of the Wavelet Transform tool is presented, and the proposed methodology is detailed. The presented results were obtained using numerically and experimentally simulated images. These results show the relevant correlation between the energy of the Wavelet coefficients and the size distribution of the analyzed objects.

scholarly journals Rotational Wavelet Filters for Analysis of Brain MRI in Detection of Alzheimer’s Disease

2021 ◽  
Vol 12 (6) ◽  
pp. 2817-2825
Author(s):  
Prof. Preeti S. Topannavar Et al.
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Wavelet Transform ◽  
Brain Mri ◽  
Rate Sensitivity ◽  
Texture Features ◽  
Complex Wavelet Transform ◽  
Wavelet Filters ◽  
Transform Coefficients ◽  
Complex Wavelet

In this paper, a method is suggested for multi directional analysis of Magnetic Resonance Image (MRI) scans for detection of Alzheimer’s disease (AD). This is a novel technique which utilizes, two-dimensional (2-D) rotated complex wavelet filters (RCWF) for feature identification. DTCWT identifies the features in 6 directions (±150±450, ±750) while RCWT identifies the features in different 6 directions (-300,0, +300, +600, +900, +1200), which enhances the directional selectivity of the transform coefficients and results in well description of corresponding textures. Dual-tree rotated complex wavelet transform (DT- RCWF) and dual-tree complex wavelet transform (DT- CWT) are applied to the sample images at a time thus the transform coefficients in twelve different directions is obtained simultaneously. The obtained transform coefficients are used for calculation of various texture features such as energy, entropy and standard deviation. The obtained parameters form the feature vectors which are given as input to the classifiers to get the input classified as Normal control or AD sufferer. This proposed algorithm produces results which are superior in terms of accuracy, feature extraction rate, sensitivity, specificity, precision and recall necessary to realize the efficiency of diagnosis of Alzheimer’s Disease as compared to other existing methods.

scholarly journals Edge detection Using Combination of Decomposition Filter and Simplified Hyperbolic Tangent Filter.docx

2020 ◽  
Author(s):  
Anand Swaminathan
Keyword(s):  
Edge Detection ◽  
High Frequency ◽  
Mean Squared Error ◽  
Performance Measure ◽  
Natural Images ◽  
Gabor Wavelet ◽  
New Combination ◽  
Hyperbolic Tangent ◽  
Edge Information ◽  
Squared Error

This paper presents a new combination of Decomposition Filters (DF) and Hyperbolic Tangent (HBT) based simplified directional operators for edge detection (ED). Conventional separable algorithms are limited in capturing the geometric features and non-separable filters eliminate these restrictions. The high-frequency band of the DF of proposed method achieves the edge information. A set of edge operators in the form of simple non-separable patterns for different scales, and orientations are applied on the edge information to capture linear edge structures and directions. While testing with ten natural images, this algorithm has improved performances in terms of reduced Mean Squared Error (MSE) in the reconstruction estimation measure. The performance measure compares the Simplified Gabor Wavelet (SGW) based ED.

A Joint Method Based on Wavelet and Curvelet Transform for Image Denoising

2012 ◽  
Vol 532-533 ◽  
pp. 758-762
Author(s):  
Hua Wang ◽  
Jian Zhong Cao ◽  
Li Nao Tang ◽  
Zuo Feng Zhou
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
High Frequency ◽  
Curvelet Transform ◽  
Experimental Results ◽  
New Method ◽  
Good Effect ◽  
Noisy Image ◽  
Joint Method ◽  
Better Than

Wavelet transform is widely used and has good effect on image denoising. Wavelet transform has unique advantages in dealing with the smooth area of image but is not so perfect in high frequency areas such as the edges. However, curvelet transform can supply this gap when dealing with the high frequency areas because of the characteristic of anisotropy. In this paper, we proposed a new method which is based on the combination of wavelet transform and curvelet transform. Firstly, we detected the edges of the noisy-image using wavelet transform. Based on the edges we divided the image into two parts: the smoothness and the edges. Then, we used different transform methods to dispose different areas of the image, wavelet threshold denoising is used in smoothness while FDCT denoising is used in edges. Experimental results showed that we could get better visual effect and higher PSNR, which indicated that the proposed method is better than using wavelet transform or curvelet transform respectively.

Wavelet-Based Pavement Distress Classification

2005 ◽  
Vol 1940 (1) ◽  
pp. 89-98 ◽  
Author(s):  
Jian Zhou ◽  
Peisen Huang ◽  
Fu-Pen Chiang
Keyword(s):  
Wavelet Transform ◽  
Radon Transform ◽  
High Frequency ◽  
High Amplitude ◽  
Experimental Results ◽  
Wavelet Domain ◽  
Wavelet Coefficients ◽  
Step Method ◽  
Space Domain ◽  
Pavement Distress

A two-step method–-wavelet transform followed by Radon transform–- is proposed for pavement distress classification. First, a pavement image is decomposed into different frequency subbands by wavelet transform. Distress is transformed into the high-amplitude wavelet coefficients, which are referred to as the details, in the high-frequency subbands. The wavelet modulus is calculated by combining the horizontal, vertical, and diagonal details. Since distress, especially crack, has dominant orientations in both the space domain and the wavelet domain, Radon transform is then applied to the wavelet modulus to transform cracks into peaks in the Radon domain. The patterns and parameters of the peaks are finally used for distress classification. Both simulated and experimental results demonstrate that the proposed two-step method is an effective method for pavement distress classification.

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