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 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.

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.

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.

Wavelet Image Denoising by Threshold Optimization Based on Genetic Algorithm

2011 ◽  
Vol 186 ◽  
pp. 337-341
Author(s):  
Shuang Ping Zhao ◽  
Xiang Wei Li ◽  
Jing Hong Xing ◽  
Yan Wen Ye
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Wavelet Transform ◽  
Image Denoising ◽  
Wavelet Decomposition ◽  
Original Signal ◽  
Denoising Method ◽  
Threshold Optimization ◽  
Wavelet Image ◽  
Inverse Transition

This paper presents a wavelet image denoising method by Threshold optimal based on wavelet transform and genetic algorithm (GA). First, using wavelet transition to a original signal and selecting a wavelet and a level of wavelet decomposition, Then the optimized thresholds of every level of wavelet decomposition will be obtained by genetic algorithms. The high coefficients at every level will be quantized. At last, inverse transition of the coefficients will be processed and we will get the final signals. An optimal image threshold using Genetic Algorithm is proposed. Compared with traditional threshold methods, the proposed method has advantages that it can implement quickly optimal threshold and have good capability and stabilization. The results show that using the proposed method can obtain satisfactory denoising effect.

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.

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