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.

A New Adaptive Threshold Image-Denoising Method Based on Edge Detection

2014 ◽  
Vol 678 ◽  
pp. 137-142 ◽  
Author(s):  
Yuan Jiao ◽  
Bin Wen Huang
Keyword(s):  
Edge Detection ◽  
Wavelet Packet ◽  
Adaptive Threshold ◽  
Difficult Problem ◽  
Denoising Method ◽  
Dependency Relation ◽  
Estimate Method ◽  
Edge Detection 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 threshold’s shortage, a new wavelet packet transform adaptive threshold image de-noising method which is based on edge detection is proposed. By edge detection method, the wavelet packet coefficients corresponding to edge which is detected and other non-edge wavelet packet coefficients are treated by different threshold. Using the relativity among wavelet packet coefficients and neighbor dependency relation, at the same time, adopting the new variance neighbor estimate method and then the 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 image’s information and the quality after image de-noising is very well.

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.

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.

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.

scholarly journals An Integrated Denoising Method for Sensor Mixed Noises Based on Wavelet Packet Transform and Energy-Correlation Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Chao Tan ◽  
Yanping Wang ◽  
Xin Zhou ◽  
Zhongbin Wang ◽  
Lin Zhang ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Processing Method ◽  
Signal Denoising ◽  
Current Signal ◽  
Energy Correlation ◽  
Denoising Method ◽  
Impact Noise ◽  
Key Technologies

In order to solve the problem of industrial sensor signal denoising, an integrated denoising method for sensor mixed noises based on wavelet packet transform and energy-correlation analysis is proposed. The architecture of proposed method is designed and the key technologies, such as wavelet packet transformation, energy-correlation analysis, and processing method of wavelet packet coefficients based on energy-correlation analysis, are presented. Finally, a simulation example for a specific signal and an application of shearer cutting current signal, which mainly contain white Gaussian noise and impact noise, are carried out, and the simulation and application results show that the proposed method is effective and is outperforming others.

Denoising Method for Calibration Data of Landing Guidance Radar Based on EMD and Wavelet

2014 ◽  
Vol 962-965 ◽  
pp. 2856-2862
Author(s):  
De Yi Sang ◽  
Jian Jun Zhao ◽  
Li Bin Yang
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Wavelet Denoising ◽  
Calibration Data ◽  
Wavelet Thresholding ◽  
Denoising Method ◽  
Calibration Process ◽  
Frequency Components

The noise resulted in the calibration process of the landing guidance radar can cause serious accidents. Analyse the principle of the EMD and wavelet denoising method. Points out the deficiencies of pure EMD or pure wavelet denoising method. Propose a denoising method based on EMD and wavelet. Improved the discriminanting method for high or low frequency components and the discriminanting method for wavelet thresholding. First EMD the signal, then denoise the high frequency components by wavelet, finally, combined the low frequency components and the denoised high frequency components to get the denoised data.

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.

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 Denoising Method for Passive Photon Counting Images Based on Block-Matching 3D Filter and Non-Subsampled Contourlet Transform

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2462
Author(s):  
Xuan Wang ◽  
Liju Yin ◽  
Mingliang Gao ◽  
Zhenzhou Wang ◽  
Jin Shen ◽  
...  
Keyword(s):  
High Frequency ◽  
Photon Counting ◽  
Low Frequency ◽  
Operation Time ◽  
Objective Evaluation ◽  
Block Matching ◽  
Contourlet Transform ◽  
Denoising Method ◽  
Low Contrast ◽  
Enhancement Method

Multi-pixel photon counting detectors can produce images in low-light environments based on passive photon counting technology. However, the resulting images suffer from problems such as low contrast, low brightness, and some unknown noise distribution. To achieve a better visual effect, this paper describes a denoising and enhancement method based on a block-matching 3D filter and a non-subsampled contourlet transform (NSCT). First, the NSCT was applied to the original image and histogram-equalized image to obtain the sub-band low- and high-frequency coefficients. Regional energy and scale correlation rules were used to determine the respective coefficients. Adaptive single-scale retinex enhancement was applied to the low-frequency components to improve the image quality. The high-frequency sub-bands whose line features were best preserved were selected and processed using a symbol function and the Bayes-shrink threshold. After applying the inverse transform, the fused photon counting image was subjected to an improved block-matching 3D filter, significantly reducing the operation time. The final result from the proposed method was superior to those of comparative methods in terms of several objective evaluation indices and exhibited good visual effects and details from the objective impression.

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