Research of Image Denoising Method about Wavelet Transform with Neighborhood Average

2014 ◽  
Vol 989-994 ◽  
pp. 4054-4057 ◽  
Author(s):  
Chen Huang
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Wavelet Denoising ◽  
Good Time ◽  
Threshold Selection ◽  
Threshold Method ◽  
Denoising Method ◽  
Time Frequency ◽  
Image Details ◽  
Denoised Image

Because wavelet transform has good time-frequency characteristics, and its application in image denoising has been promising. Firstly, use the threshold method of the wavelet transform is used in removing image noise, and then the denoised image is smoothed using neighborhood average filtering with Gauss template. And wavelet denoising process and domain threshold selection principle are discussed. Simulation results show that this method can effectively reduce the noise and can remain most of image details better.

Denoising Vibration Signals of Food Refrigerant Air Compressor Based on Wavelet Transform

2010 ◽  
Vol 439-440 ◽  
pp. 1037-1041 ◽  
Author(s):  
Yan Jue Gong ◽  
Zhao Fu ◽  
Hui Yu Xiang ◽  
Li Zhang ◽  
Chun Ling Meng
Keyword(s):  
Wavelet Transform ◽  
Frequency Characteristic ◽  
Vibration Signal ◽  
Wavelet Denoising ◽  
Signal Denoising ◽  
Denoising Method ◽  
Vibration Signals ◽  
Air Compressor ◽  
Time Frequency ◽  
Soft Threshold

On the basis of wavelet denoising and its better time-frequency characteristic, this paper presents an effective vibration signal denoising method for food refrigerant air compressor. The solution of eliminating strong noise is investigated with the combination of soft threshold and exponential lipschitza. The good denoising results show that the presented method is effective for improving the signal noise ratio and builds the good foundation for further extraction of the vibration signals.

Comparison and Improvements of Image Denoising Based on Wavelet Transform

2015 ◽  
Vol 740 ◽  
pp. 644-647
Author(s):  
Xue Mei Xiao
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Image Denoising ◽  
Wavelet Denoising ◽  
Important Application ◽  
Denoising Method ◽  
Advantages And Disadvantages ◽  
Wavelet Transform Modulus Maxima ◽  
Modulus Maxima

Wavelet transform denoising is an important application of wavelet analysis in signal and image processing. Several popular wavelet denoising methods are introduced including the Mallat forced denoising, the wavelet transform modulus maxima method and the nonlinear wavelet threshold denoising method. Their advantages and disadvantages are compared, which may be helpful in selecting the wavelet denoising methods. At the same time, several improvement methods are offered.

A Novel Image Denoising Method in 2-D Fractional Time-Frequency Domain

2015 ◽  
Vol 734 ◽  
pp. 586-589
Author(s):  
Shuang Shuang He ◽  
Yuan Yuan Jiang ◽  
Jin Yan Zheng
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Image Process ◽  
Denoising Method ◽  
Improve Image Quality ◽  
Time Frequency ◽  
Fractional Wavelet Transform

To improve image quality and a higher level of follow-up image process needed, it's of great importance to do the image denoising process first. A new image denoising method in two-dimensional (2-D) fractional time-frequency domain is proposed in this paper. Through the realization of 2-D fractional wavelet transform algorithm, the 2-D fractional wavelet transform theory is applied to image denoising, and compare with image denoising method based on 2-D wavelet transform. A large number of image denoising simulation studies have shown that, the Peak Signal to Noise Ratio of output images based on the proposed method can be effectively improved, and preserve detail information effectively and reduce the noise at the same time. It proved 2-D fractional wavelet transform is a new and effective time-frequency domain image denoising method.

Research on Denoising of GPS Data Based on Nonlinear Wavelet Transform Threshold Method

2012 ◽  
Vol 446-449 ◽  
pp. 926-936
Author(s):  
De Bao Yuan ◽  
Xi Min Cui ◽  
Guo Wang ◽  
Jing Jing Jin ◽  
Wan Yang Xu
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Wavelet Denoising ◽  
Systematic Research ◽  
Gps Data ◽  
Signal Denoising ◽  
Threshold Method ◽  
Denoising Method

Signal denoising is one of the classic problems in the field of signal processing. As a new kind of signal processing tool, the good denoising performance of wavelet analysis has caused public growing concern and attention. The paper does systematic research on nonlinear wavelet threshold denoising method. And the wavelet denoising method has been used on GPS signal, and good results have been achieved.

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 innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

2011 ◽  
Vol 1 (3) ◽  
Author(s):  
T. Sumathi ◽  
M. Hemalatha
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Curvelet Transform ◽  
Relevant Information ◽  
Good Time ◽  
Discrete Wavelet ◽  
Fusion Algorithm ◽  
Time Frequency ◽  
Fused Image

AbstractImage fusion is the method of combining relevant information from two or more images into a single image resulting in an image that is more informative than the initial inputs. Methods for fusion include discrete wavelet transform, Laplacian pyramid based transform, curvelet based transform etc. These methods demonstrate the best performance in spatial and spectral quality of the fused image compared to other spatial methods of fusion. In particular, wavelet transform has good time-frequency characteristics. However, this characteristic cannot be extended easily to two or more dimensions with separable wavelet experiencing limited directivity when spanning a one-dimensional wavelet. This paper introduces the second generation curvelet transform and uses it to fuse images together. This method is compared against the others previously described to show that useful information can be extracted from source and fused images resulting in the production of fused images which offer clear, detailed information.

Research on Image Denoising Method Based on Wavelet Transform

2018 ◽  
Author(s):  
Jun Lei Song ◽  
Mei Juan Chen ◽  
Chang Jiang ◽  
Yan Xia Huang ◽  
Qi Liu ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Denoising Method

scholarly journals LSTM with Wavelet Transform Based Data Preprocessing for Stock Price Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaodan Liang ◽  
Zhaodi Ge ◽  
Liling Sun ◽  
Maowei He ◽  
Hanning Chen
Keyword(s):  
Wavelet Transform ◽  
Stock Price ◽  
Signal Reconstruction ◽  
Data Preprocessing ◽  
Original Data ◽  
Training Data ◽  
Threshold Method ◽  
Denoising Method ◽  
Stock Price Prediction ◽  
Price Prediction

For profit maximization, the model-based stock price prediction can give valuable guidance to the investors. However, due to the existence of the high noise in financial data, it is inevitable that the deep neural networks trained by the original data fail to accurately predict the stock price. To address the problem, the wavelet threshold-denoising method, which has been widely applied in signal denoising, is adopted to preprocess the training data. The data preprocessing with the soft/hard threshold method can obviously restrain noise, and a new multioptimal combination wavelet transform (MOCWT) method is proposed. In this method, a novel threshold-denoising function is presented to reduce the degree of distortion in signal reconstruction. The experimental results clearly showed that the proposed MOCWT outperforms the traditional methods in the term of prediction accuracy.

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.

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