scholarly journals A New Texture Synthesis Algorithm Based on Wavelet Packet Tree

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Hsi Chin Hsin ◽  
Tze-Yun Sung ◽  
Yaw-Shih Shieh ◽  
Carlo Cattani
Keyword(s):  
High Frequency ◽  
Texture Synthesis ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Computation Time ◽  
Input Image ◽  
Synthesis Algorithm ◽  
Multiresolution Representation ◽  
Hierarchical Trees ◽  
Fractal Images

This paper presents an efficient texture synthesis based on wavelet packet tree (TSWPT). It has the advantage of using a multiresolution representation with a greater diversity of bases functions for the nonlinear time series applications such as fractal images. The input image is decomposed into wavelet packet coefficients, which are rearranged and organized to form hierarchical trees called wavelet packet trees. A 2-step matching, that is, coarse matching based on low-frequency wavelet packet coefficients followed by fine matching based on middle-high-frequency wavelet packet coefficients, is proposed for texture synthesis. Experimental results show that the TSWPT algorithm is preferable, especially in terms of computation time.

scholarly journals Fast Texture Synthesis in Adaptive Wavelet Packet Trees

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Ying-Shen Juang ◽  
Hsi-Chin Hsin ◽  
Tze-Yun Sung ◽  
Carlo Cattani
Keyword(s):  
High Frequency ◽  
Texture Synthesis ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Computation Time ◽  
Wavelet Packet Transform ◽  
Synthesis Process ◽  
Adaptive Wavelet ◽  
Hierarchical Trees ◽  
Speed Up

Wavelet packet transform known as a substantial extension of wavelet transform has drawn a lot of attention to visual applications. In this paper, we advocate using adaptive wavelet packet transform for texture synthesis. The adaptive wavelet packet coefficients of an image are organized into hierarchical trees called adaptive wavelet packet trees, based on which an efficient algorithm has been proposed to speed up the synthesis process, from the low-frequency tree nodes representing the global characteristics of textures to the high-frequency tree nodes representing the local details. Experimental results show that the texture synthesis in the adaptive wavelet packet trees (TSIAWPT) algorithm is suitable for a variety of textures and is preferable in terms of computation time.

scholarly journals Direction-aware Feature-level Frequency Decomposition for Single Image Deraining

2021 ◽  
Author(s):  
Sen Deng ◽  
Yidan Feng ◽  
Mingqiang Wei ◽  
Haoran Xie ◽  
Yiping Chen ◽  
...  
Keyword(s):  
High Frequency ◽  
State Of The Art ◽  
Low Frequency ◽  
Communication Channels ◽  
Input Image ◽  
Experimental Results ◽  
Training Procedure ◽  
Single Image ◽  
Frequency Decomposition

We present a novel direction-aware feature-level frequency decomposition network for single image deraining. Compared with existing solutions, the proposed network has three compelling characteristics. First, unlike previous algorithms, we propose to perform frequency decomposition at feature-level instead of image-level, allowing both low-frequency maps containing structures and high-frequency maps containing details to be continuously refined during the training procedure. Second, we further establish communication channels between low-frequency maps and high-frequency maps to interactively capture structures from high-frequency maps and add them back to low-frequency maps and, simultaneously, extract details from low-frequency maps and send them back to high-frequency maps, thereby removing rain streaks while preserving more delicate features in the input image. Third, different from existing algorithms using convolutional filters consistent in all directions, we propose a direction-aware filter to capture the direction of rain streaks in order to more effectively and thoroughly purge the input images of rain streaks. We extensively evaluate the proposed approach in three representative datasets and experimental results corroborate our approach consistently outperforms state-of-the-art deraining algorithms.

scholarly journals IMAGE RECONSTRUCTION BASED ON COMBINATION OF WAVELET DECOMPOSITION, INPAINTING AND TEXTURE SYNTHESIS

2009 ◽  
Vol 09 (01) ◽  
pp. 51-65 ◽  
Author(s):  
HUAWEI CHEN ◽  
ICHIRO HAGIWARA ◽  
A. KIET TIEU
Keyword(s):  
High Frequency ◽  
Wavelet Decomposition ◽  
Color Image ◽  
Texture Synthesis ◽  
Low Frequency ◽  
Surface Interpolation ◽  
Digital Reconstruction ◽  
Large Size ◽  
Frequency Level ◽  
Digital Inpainting

Digital inpainting provides a means for reconstruction of damaged portions of an image. Although the inpainting basics are straightforward, most inpainting techniques published in the literature are only suitable for remarkable small portion or smooth color image. In order to avoid such shortcomings, we present a new algorithm for digital reconstruction based on combination of wavelet decomposition, surface-based/PDE-based inpainting and texture synthesis. In this algorithm, wavelet transform firstly decomposes the image into high frequency and low frequency level parts. Subsequently, CSRBF which is generally used for surface interpolation or PDE-based inpainting is employed for low frequency level and texture synthesis is used for high frequency level. It results in that not only slight portion but also the common blotched image can be reconstructed with high quality. Especially, our algorithm makes large-size blotched image possible and becomes more efficient as compared to individual PDE-based and CSRBF approaches.

A New Adaptive Image Denoising Method Based on Wavelet Packet Transform and Neighbor Dependency

2013 ◽  
Vol 433-435 ◽  
pp. 301-305
Author(s):  
Bin Wen Huang ◽  
Yuan Jiao
Keyword(s):  
High Frequency ◽  
Wavelet Packet ◽  
Estimation Method ◽  
Low Frequency ◽  
Adaptive Threshold ◽  
Difficult Problem ◽  
Wavelet Packet Transform ◽  
Denoising Method ◽  
Image Edges ◽  
Orthogonal Wavelet Transform

In image processing, removal of noise without blurring the image edges is a difficult problem. Aiming at orthogonal wavelet transform and traditional thresholds shortage, a new adaptive threshold image de-noising method which is based on wavelet packet transform and neighbor dependency is proposed. Low frequency part and high frequency part can be decomposed at the same time in wavelet packet transform and the information contained in wavelet coefficients is redundant. Using this kind of relativity in wavelet packet coefficients, we use a new variance neighbor estimation method and then neighbor dependency adaptive threshold is produced. From the experiment result, we see that compared with traditional methods, this method can not only effectively eliminate noise, but can also well keep original images information and the quality after image de-noising is very well.

scholarly journals UHF PD Experiments on Real Power Transformer and its Frequency Characteristics Results

2016 ◽  
Vol 6 (1) ◽  
pp. 906-912
Author(s):  
H. Fan ◽  
J. Hu ◽  
H. Liu ◽  
Y. Yin ◽  
M. Danikas
Keyword(s):  
High Frequency ◽  
Power Transformer ◽  
Detection System ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Recognition System ◽  
Frequency Range ◽  
Real Power ◽  
The Relationship ◽  
Detection And Recognition

A number of methods have been used in partial discharge (PD) detection and recognition. Among these methods, ultra-high frequency (UHF) detection and recognition based on a single signal have attracted much attention. In this paper, a UHF PD detection system is built, and samples are acquired through experiments on a real power transformer. The received signal is decomposed into different frequency ranges through wavelet packet decomposition (WPD). In each frequency range, a pattern recognition neural network is built, and then the relationship between the information in that frequency range and PD type is described. By comparing the recognition accuracy of these networks, frequency range selection is optimized. In this specific case (the specific transformer, PD sources, and UHF sensors), results show that low frequency (156.25 MHz to 312.5 MHz) and high frequency ranges (1093.75 MHz to 1250 MHz) contain the most information for recognition. If a PD detection recognition system is to be designed, then the performance around these frequency ranges should be given attention.

scholarly journals Case Studies on Discrete Wavelet Denoising via Kernel Based Nonlinear Component Analysis

2021 ◽  
Vol 9 ◽  
pp. 8-12
Author(s):  
Zhengmao Ye ◽  
Hang Yin ◽  
Yongmao Ye
Keyword(s):  
Nonlinear Systems ◽  
Case Studies ◽  
High Frequency ◽  
Linear Models ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Component Analysis ◽  
Wavelet Denoising ◽  
Discrete Wavelet ◽  
Nonlinear Component

Complex real world problems are essentially nonlinear. Linear models are relatively simple but inaccurate to describe the nonlinear aspects of dynamic system behaviors. Denoising techniques have been broadly applied to numerous applications in the spatial domain, frequency domain, and time domain. To increase the adaptability of denoising techniques to signal processing of arbitrary nonlinear systems, kernel based nonlinear component analysis is proposed to enhance wavelet denoising. In the multilevel wavelet decomposition, the low frequency approximations and high frequency details are produced at each level. Discrete wavelet transform (DWT) will help to decompose low frequency approximations exclusively at all the succeeding levels, while wavelet packet transform decomposes both approximations and high frequency details at each level. DWT is selected for wavelet denoising in this study, where details at each level and the approximation at specified level are all subject to simplification using nonlinear component analysis. Case studies of typical nonlinear denoising problems in various domains are conducted. The results manifest strong feasibility and adaptability across diverse denoising problems of nonlinear systems.

Fusion Method of Infrared and Visible Images Based on Wavelet Packet Transform

2013 ◽  
Vol 333-335 ◽  
pp. 1134-1138
Author(s):  
Hai Tao Su ◽  
Zhan Feng Wang ◽  
Zhi Yi Hu ◽  
Hong Shu Chen ◽  
Jie Liang Wang
Keyword(s):  
Standard Deviation ◽  
Image Fusion ◽  
High Frequency ◽  
Wavelet Decomposition ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Wavelet Packet Transform ◽  
Wavelet Function ◽  
Decomposition Level ◽  
Feature Information

The multi-sensor image fusion is the effective practices to increase the image information, highlight the detection superiority, reduce fuzzy understanding and to reduce data redundancy. Image fusion based on wavelet transform, the image wavelet decomposition processing only exists in the low-frequency, when the image contains high-frequency information, such as a large number of small edge or texture, which can not extract the feature information of the image, so resulting in the fusion is ineffective. In response to these problems, the use of image fusion algorithm based on wavelet packet transform, continue to break down, while the low-frequency further decomposition of the high-frequency of the image, extracts image feature information more effectively. In the same conditions of wavelet function, decomposition level, the fusion policy, comparative analysis has been researched on wavelet transform and wavelet packet transform on the same parameters of the information entropy, average gradient, standard deviation, spatial frequency, the results show that, image fusion of the algorithm based on wavelet packet transform are the highest and the better. In the other hand, in order to investigate the fusion effectiveness of the decomposition level on the same wavelet function conditions, fusion image parameters, such as entropy, average gradient, standard deviation, and spatial frequency, have been calculated using the db3 wavelet function corresponding to the decomposition level 1-5. The results show that the fusion effectiveness should achieve the best with wavelet decomposition level of 3 or 2.

The Research on Multiscale Image Fusion Based on the Dualtree Complex Wavelet

2013 ◽  
Vol 748 ◽  
pp. 600-604
Author(s):  
Yi Luo ◽  
Gui Ling Yao ◽  
Wei Fan Wang
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Fusion Rule ◽  
Input Image ◽  
Image Similarity ◽  
Source Image ◽  
Multi Scale ◽  
Rapid Extraction ◽  
Fusion Coefficient

In order to effectively ease and solve fusion effect and the contradiction of the algorithm complexity, this paper puts forward a fusion rule on rapid extraction of multi-scale fusion coefficient, this fusion rules first used in the source image multi-scale decomposition of the scale fusion is the extraction of coefficient based on the neighborhood the fusion of window way, the low frequency of the improved neighborhood entropy to extract matching measure (that is, between the input image similarity degree), high frequency with the cross scale neighborhood gradient to extract matching measure, and gives the fusion coefficient formula. Because of the wavelet transform has moved degeneration, this paper puts forward the application of double tree after wavelet transform to do image multi-scale decomposition.

Acoustic Emission Behaviors of Carbon-Cloth/Epoxy Composites in Compression Test

2011 ◽  
Vol 480-481 ◽  
pp. 421-426
Author(s):  
Chao Lu ◽  
Peng Ding ◽  
Zheng Hua Chen
Keyword(s):  
Acoustic Emission ◽  
Compression Test ◽  
High Frequency ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Matrix Cracking ◽  
Carbon Cloth ◽  
High Frequency Range ◽  
Frequency Range ◽  
The Matrix

In this paper, we use acoustic emission (AE) system to collect the AE signals and analyze the damage evolution during the monotonic compression test. Based on the experimental correlation diagram of the load and characters of the acoustic emission, the reference load of failure was found. The experimental results also revealed the characters of the source of the acoustic emission signals after the wavelet packet decomposition and frequency spectrum analysis. The frequency range of the matrix cracking is on the range of 125~187.5 kHz, while the frequency range of layer debonding is wide, it is not just on the low-frequency range but on the high-frequency range. The frequency of fiber breakage is on the high frequency range, nearly on the range of 375~437.5 kHz.

scholarly journals Denoising GPS-Based Structure Monitoring Data Using Hybrid EMD and Wavelet Packet

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Lu Ke
Keyword(s):  
High Frequency ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Monitoring Data ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Structure Monitoring ◽  
Energy Leakage ◽  
Frequency Components ◽  
Different Characteristics

High-frequency components are often discarded for data denoising when applying pure wavelet multiscale or empirical mode decomposition (EMD) based approaches. Instead, they may raise the problem of energy leakage in vibration signals. Hybrid EMD and wavelet packet (EMD-WP) is proposed to denoise Global Positioning System- (GPS-) based structure monitoring data. First, field observables are decomposed into a collection of intrinsic mode functions (IMFs) with different characteristics. Second, high-frequency IMFs are denoised using the wavelet packet; then the monitoring data are reconstructed using the denoised IMFs together with the remaining low-frequency IMFs. Our algorithm is demonstrated on a synthetic displacement response of a 3-story frame excited by El Centro earthquake along with a set of Gaussian random white noises on different levels added. We find that the hybrid method can effectively weaken the multipath effect with low frequency and can potentially extract vibration feature. However, false modals may still exist by the rest of the noise contained in the high-frequency IMFs and when the frequency of the noise is located in the same band as that of effective vibration. Finally, real GPS observables are implemented to evaluate the efficiency of EMD-WP method in mitigating low-frequency multipath.

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