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 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.

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.

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.

scholarly journals Final Consonant Segmentation for Thai syllable by Using Vowel Characteristics and Wavelet Packet Transform

1970 ◽  
Vol 1 (1) ◽  
pp. 50-62
Author(s):  
Kosin Chamnongthai ◽  
Wudthipong Pichitwong ◽  
Piyasawat Navaratana Na Ayudhya
Keyword(s):  
High Frequency ◽  
Recognition Accuracy ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Wavelet Packet Transform ◽  
Frequency Range ◽  
Frequency Signal ◽  
Short Period ◽  
Vowel Phoneme ◽  
Consonant Phoneme

Since Thai final consonant is unique comparing with other languages and plays key role in recognizing the Thai syllables, segmentation of the final consonant phoneme from the vowel is needed and capable of decreasing the amount of recognition patterns and also improving the recognition accuracy. This paper presents a technique to separate the final consonant phoneme from Thai syllable by exploiting the vowel characteristics and Wavelet packet transform. In this method, ending of the vowel phoneme (starting of the final consonant) is considered by vowel characteristic, which has the highest energy in the syllable. The frequency range having this qualification is selected as vowels. It is then employed to determine the filter for vowel signal. The Wavelet packet transform that is appropriate for discriminating vowel (high frequency and long period) from final consonant phoneme (low frequency and short period) is used as the filter. And the ending of vowels frequency signal component is considered to be the segmentation point of the final consonant. The experiments have been performed by 4,350 samples of syllable recorded from 15 males and 15 females. The experimental results gained the 92.89 % accuracy.

Tradeoff Theory and Leverage Dynamics of High-Frequency Debt Issuers*

2020 ◽  
Author(s):  
B Espen Eckbo ◽  
Michael Kisser
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Debt Financing ◽  
Trade Off ◽  
Speed Of Adjustment ◽  
Industrial Companies ◽  
Long Run ◽  
Speed Up ◽  
Target Leverage ◽  
Leverage Dynamics

Abstract We test whether high-frequency net-debt issuers (HFIs)—public industrial companies with relatively low issuance costs and high debt-financing benefits—manage leverage toward long-run targets. Our answer is they do not: (1) the leverage–profitability correlation is negative even in quarters with leverage rebalancing; (2) the speed-of-adjustment to target leverage deviations is no higher for HFIs than for low-frequency net-debt issuers; and (3) under-leveraged HFIs do not speed up rebalancing activity in significant investment periods. Thus, even in the subset of firms most likely to follow dynamic trade-off theory, the theory does not appear to hold.

scholarly journals Degraded Image Enhancement Using Dual-Domain-Adaptive Wavelet and Improved Fuzzy Transform

2020 ◽  
Author(s):  
Yuehua Huo ◽  
Weiqiang Fan ◽  
Xiaoyu Li
Keyword(s):  
Performance Evaluation ◽  
Image Enhancement ◽  
High Frequency ◽  
Low Frequency ◽  
Original Image ◽  
Fuzzy Transform ◽  
Adaptive Wavelet ◽  
Comprehensive Performance ◽  
Degraded Image ◽  
Dual Domain

Abstract A novel enhancement algorithm of degraded image based on dual-domain-adaptive wavelet and improved fuzzy transform is proposed, aiming at the problem of surveillance videos degradation caused by complex lighting conditions underground. The dual-domain filtering (DDF) is used to decompose the image into low-frequency sub-image and high-frequency sub-images. The contrast limited adaptive histogram enhancement (CLAHE) is used to adjust the overall brightness and contrast of the low-frequency sub-image. Discrete wavelet transform (DWT) is used to obtain low frequency sub-band (LFS) and high frequency sub-band (HFS). The wavelet shrinkage threshold method based on Bayesian estimation is used to calculate the wavelet threshold corresponding to the HFS at different scales. A Garrate threshold function that introduces adaptive adjustment factor and enhancement coefficient is designed to adaptively de-noise and enhance the HFS coefficients corresponding to wavelet thresholds at different scales. Meanwhile, the gamma function is used to realize the correction of the LFS coefficients. The constructed PAL fuzzy enhancement operator is used to perform contrast enhancement and highlight area suppression on the reconstructed image to obtain an enhanced image. The proposed algorithm is evaluated by subjective vision and objective indicators. The experimental results show that the proposed algorithm can significantly improve the overall brightness and contrast of the original image, suppress noise of dust & spray, enhance the image details and improve the visual effect of the original image. Compared with the images enhanced by the STFE, GTFE, CLAHE, SSR, MSR, DGR, and MSWT algorithms, the comprehensive performance evaluation indicators of the images enhanced by the proposed algorithm are increased by 312.50%, 34.69%, 53.49%, 22.22%, 32.00%, 10.00%, 60.98%, 3.13%, respectively. At the same time, comprehensive performance evaluation indicator of the enhance image and the robustness is the best, which is more suitable for image enhancement in different mine environments.

scholarly journals IMAGE HIDING ON HIGH FREQUENCY SPEECH COMPONENTS USING WAVELET PACKET TRANSFORM

2018 ◽  
Vol 44 (1) ◽  
pp. 36-39
Author(s):  
Mohammed Al-Turfi
Keyword(s):  
Speech Signal ◽  
High Frequency ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Image Hiding ◽  
The Media ◽  
Embedded Image ◽  
Reconstruction Rate

This paper propose a method for security threw hiding the image inside the speech signal by replacing the high frequencycomponents of the speech signal with the data of the image where the high frequency speech components are separated and analyzed usingthe Wavelet Packet Transform (WPT) where the new signal will be remixed to create a new speech signal with an embedded image. The algorithm is implemented on MATLAB 15 and is designed to achieve best image hiding where the reconstruction rate was more than 94% while trying to maintain the same size of the speech signal to overcome the need for a powerful channel to handle the task. Best results were achieved with higher speech resolution (higher number of bits per sample) and longer periods (higher number of samples in the media file).

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.

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