A Joint Method Based on Wavelet and Curvelet Transform for Image Denoising

2012 ◽  
Vol 532-533 ◽  
pp. 758-762
Author(s):  
Hua Wang ◽  
Jian Zhong Cao ◽  
Li Nao Tang ◽  
Zuo Feng Zhou
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
High Frequency ◽  
Curvelet Transform ◽  
Experimental Results ◽  
New Method ◽  
Good Effect ◽  
Noisy Image ◽  
Joint Method ◽  
Better Than

Wavelet transform is widely used and has good effect on image denoising. Wavelet transform has unique advantages in dealing with the smooth area of image but is not so perfect in high frequency areas such as the edges. However, curvelet transform can supply this gap when dealing with the high frequency areas because of the characteristic of anisotropy. In this paper, we proposed a new method which is based on the combination of wavelet transform and curvelet transform. Firstly, we detected the edges of the noisy-image using wavelet transform. Based on the edges we divided the image into two parts: the smoothness and the edges. Then, we used different transform methods to dispose different areas of the image, wavelet threshold denoising is used in smoothness while FDCT denoising is used in edges. Experimental results showed that we could get better visual effect and higher PSNR, which indicated that the proposed method is better than using wavelet transform or curvelet transform respectively.

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.

MEMS Gyro Signal De-Noising Based on Adaptive Stationary Wavelet Threshold

2012 ◽  
Vol 466-467 ◽  
pp. 986-990 ◽  
Author(s):  
Xing Hui Yang ◽  
Jian Xin Ren ◽  
Xing Mei Zhao ◽  
Ran Chen
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Gibbs Phenomenon ◽  
Adaptive Threshold ◽  
Experimental Results ◽  
Stationary Wavelet Transform ◽  
Random Drift ◽  
Mems Gyroscope ◽  
Time Invariant ◽  
Better Than

Random drift is a significant index that can affect the precision of MEMS gyroscope. It is one of the important techniques to decrease the random drift error in improving the precision of MEMS gyro. According to analyzing the principle of traditional Wavelet Transform and Stationary Wavelet Transform, Stationary Wavelet Transform (SWT) is adopted to de-noise the signal of MEMS gyro. Due to SWT’s time-invariant, the Gibbs phenomenon is decreased. SWT with adaptive threshold is adopted to analyze the actual dynamic MEMS gyroscope data. The experimental results show that this presented method is better than traditional wavelet threshold de-noising methods. It can effectively restrain the noise in high frequency and improve the drift error of MEMS gyro.

scholarly journals An Image Denoising Algorithm Based On Curvelet Transform

2016 ◽  
Vol 12 (1) ◽  
pp. 5827-5831
Author(s):  
Yin Kaikai ◽  
Su Bo
Keyword(s):  
Mathematical Method ◽  
Wavelet Transform ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Curvelet Transform ◽  
Experimental Results ◽  
High Peak ◽  
Signal To Noise ◽  
Visual Effect ◽  
Good Image

Aiming at the limitations of the wavelet transform in image denoising, this paper proposes a new image denoising algorithm based on curvelet transform mathematical method. In this paper, the feasibility of this method is proved by the experimental results. The experiment result shows that, using the proposed new algorithm can get high peak signal to noise ratio, visual effect is very good image.

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.

The Development of Wavelet Transform and its Application in Image Denoise

2013 ◽  
Vol 694-697 ◽  
pp. 2003-2008
Author(s):  
Ming Hong Dai
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Filter Bank ◽  
Experimental Studies ◽  
Visual Quality ◽  
Simulation Experiments ◽  
Design And Implementation ◽  
Direction Filter ◽  
Related Design ◽  
Better Than

The paper introduces Laplace pyramid, Ridgelet and Curvelet principle, structure and methods, and their denoising experimental studies. It also introduces the traditional direction filter of principle, structure and methodology, and the simulation experiments show that its image denoising PSNR is slightly lower than wavelet but denoising image visual quality is better than former. To that end, proposed a new direction filters that uniform direction filter banks and non-uniform direction filters, proved filter passband condition and related design and implementation issues were discussed. nonlinear experiment shows that the new direction filter bank was better than the wavelet.

scholarly journals Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhuxiang Shen ◽  
Wei Li ◽  
Hui Han
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Similarity Index ◽  
Speckle Noise ◽  
Disease Diagnosis ◽  
Function Optimization ◽  
Threshold Function ◽  
Ultrasound Images ◽  
Better Than

To explore the utilization of the convolutional neural network (CNN) and wavelet transform in ultrasonic image denoising and the influence of the optimized wavelet threshold function (WTF) algorithm on image denoising, in this exploration, first, the imaging principle of ultrasound images is studied. Due to the limitation of the principle of ultrasound imaging, the inherent speckle noise will seriously affect the quality of ultrasound images. The denoising principle of the WTF based on the wavelet transform is analyzed. Based on the traditional threshold function algorithm, the optimized WTF algorithm is proposed and applied to the simulation experiment of ultrasound images. By comparing quantitatively and qualitatively with the traditional threshold function algorithm, the advantages of the optimized WTF algorithm are analyzed. The results suggest that the image is denoised by the optimized WTF. The mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM) of the images are 20.796 dB, 34.294 dB, and 0.672 dB, respectively. The denoising effect is better than the traditional threshold function. It can denoise the image to the maximum extent without losing the image information. In addition, in this exploration, the optimized function is applied to the actual medical image processing, and the ultrasound images of arteries and kidneys are denoised separately. It is found that the quality of the denoised image is better than that of the original image, and the extraction of effective information is more accurate. In summary, the optimized WTF algorithm can not only remove a lot of noise but also obtain better visual effect. It has important value in assisting doctors in disease diagnosis, so it can be widely applied in clinics.

Effect of Microwave Drying on Dyeing Rate of Cotton Yarn

2011 ◽  
Vol 328-330 ◽  
pp. 1619-1622 ◽  
Author(s):  
Zi Na Zhu ◽  
Zhuo Meng ◽  
Guang Chao An ◽  
Yi Ze Sun
Keyword(s):  
Liquid Ammonia ◽  
Engineering Application ◽  
Experimental System ◽  
New Method ◽  
Microwave Drying ◽  
Good Effect ◽  
Cotton Yarn ◽  
Steam Drying ◽  
Dyeing Rate ◽  
Better Than

Focusing on the residual amount of liquid ammonia in modified cotton yarn, this paper presents a new method of drying the liquid ammonia by microwave. An experimental system is designed to find whether this new method is correct and to analyse the effect of microwave drying on the dyeing rate of cutton yarn. Firstly, the microwave drying experiment is carried out to prove that this new drying method is better than the traditional steam drying way. Then, another contrast experiment on the dyeing rate confirms the good effect of microwave drying. So it is concluded that microwave drying is an efficient and energy-saving way to remove the remaining liquid ammonia. Meanwhile, the experimental results provide the relevant data to make guiding sense to engineering application.

Wavelet Transform with a Novel Integration Technique for Image Fusion

2011 ◽  
Vol 204-210 ◽  
pp. 1419-1422 ◽  
Author(s):  
Yong Yang
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Experimental Results ◽  
Integration Technique ◽  
Frequency Bands ◽  
Fusion Methods ◽  
Fusion Scheme ◽  
Measures Of Performance

Image fusion is to combine several different source images to form a new image by using a certain method. Recent studies show that among a variety of image fusion algorithms, the wavelet-based method is more effective. In the wavelet-based method, the key technique is the fusion scheme, which can decide the final fused result. This paper presents a novel fusion scheme that integrates the wavelet decomposed coefficients in a quite separate way when fusing images. The method is formed by considering the different physical meanings of the coefficients in both the low frequency and high frequency bands. The fused results were compared with several existing fusion methods and evaluated by three measures of performance. The experimental results can demonstrate that the proposed method can achieve better performance than conventional image fusion methods.

Noisy Image Segmentation Based on Artificial Bee Colony Algorithm

2014 ◽  
Vol 687-691 ◽  
pp. 3652-3655
Author(s):  
Yong Hao Xiao ◽  
Zhuo Bin He ◽  
Yao Hu ◽  
Wei Yu Yu
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Experimental Results ◽  
New Method ◽  
Noisy Image ◽  
Noisy Images ◽  
Bee Colony

Segmentation of noisy images is one of the most challenging problems in image analysis. It hasn’t yet been solved very well. In this paper, we propose a new method for image segmentation, which is able to segment two kinds of noisy images. The experimental results prove that Artificial Bee Colony Algorithm performs better for two types of noisy images.

scholarly journals Digital Watermarking using Multiscale Ridgelet Transform

2013 ◽  
pp. 162-167
Author(s):  
Manmit Kaur ◽  
H. P. Sinha
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Edge Detection ◽  
Discrete Cosine Transform ◽  
Digital Watermarking ◽  
High Frequency ◽  
Digital Images ◽  
Experimental Results ◽  
Ridgelet Transform ◽  
Frequency Bands

The multi-resolution watermarking method for digital images proposed in this work. The multiscale ridgelet coefficients of low and high frequency bands of the watermark is embedded to the most significant coefficients at low and high frequency bands of the multiscale ridgelet of an host image, respectively. A multi-resolution nature of multiscale ridgelet transform is exploiting in the process of edge detection. Experimental results of the proposed watermarking method are compared with the previously available watermarking algorithm wavelet transform. Moreover, the proposed watermarking method also tested on images attached by Discrete Cosine Transform (DCT) and wavelet based lossy image compression techniques.

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