scholarly journals Quaternion Wavelet Analysis and Application in Image Denoising

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Ming Yin ◽  
Wei Liu ◽  
Jun Shui ◽  
Jiangmin Wu
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Basis Function ◽  
Signal To Noise Ratio ◽  
Scale Space ◽  
Analysis Tool ◽  
Magnitude Distribution ◽  
Wavelet Space ◽  
Quaternion Wavelet Transform ◽  
Gauss Distribution

The quaternion wavelet transform is a new multiscale analysis tool. Firstly, this paper studies the standard orthogonal basis of scale space and wavelet space of quaternion wavelet transform in spatialL2(R2), proves and presents quaternion wavelet’s scale basis function and wavelet basis function concepts in spatial scale spaceL2(R2;H), and studies quaternion wavelet transform structure. Finally, the quaternion wavelet transform is applied to image denoising, and generalized Gauss distribution is used to model QWT coefficients’ magnitude distribution, under the Bayesian theory framework, to recover the original coefficients from the noisy wavelet coefficients, and so as to achieve the aim of denoising. Experimental results show that our method is not only better than many of the current denoising methods in the peak signal to noise ratio (PSNR), but also obtained better visual effect.

scholarly journals Denoising Images by Dual–Tree Complex Wavelet Transform Combined with Meta Heuristic Optimization Algorithms

2020 ◽  
Vol 9 (4) ◽  
pp. 2621-2627
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Optimization Algorithms ◽  
Optimization Technique ◽  
Threshold Value ◽  
Heuristic Optimization ◽  
Algorithm Optimization ◽  
Complex Wavelet Transform ◽  
Complex Wavelet

Denoising is a prime objective technique for processing images. Image denoising techniques removes the noises present in an image without interrupting its features and contents. The image gets interrupted by channel or processing noise depending on the applications. Thus, the contaminated noises produce degradable image qualities with respect to subjective and objective approach. To overcome this, image denoising approaches were suggested. In the present research, Dual–Tree Complex Wavelet transform (DTCWT) is utilized to achieve image denoising since they perform multi resolution decomposition by two DWT trees. Soft and hard thresholding methods are used to threshold wavelet coefficients. The present research proposes a novel technique to denoise images which gives image information clearly by thresholding and optimization technique. The optimization is carried through different Meta-heuristic optimization Algorithms Genetic Algorithm (GA) and Grey-wolf optimization (GWO) algorithm. Optimization of threshold value is performed after Bayesian method and the observed output produces better results when compared to other techniques involving Visu shrink, Sure shrink and Bayes shrinkbased on peak signal to noise ratio (PSNR) and visual qualities.

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.

Image Denoising Using Wavelet Transform and Wavelet Transform with Enhanced Diversity

2011 ◽  
Vol 403-408 ◽  
pp. 866-870
Author(s):  
Vaibhav Nigam ◽  
Smriti Bhatnagar ◽  
Sajal Luthra
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Image Denoising ◽  
Wavelet Transforms ◽  
Signal To Noise Ratio ◽  
Practical Implementation ◽  
Discrete Wavelet ◽  
Maximum Output ◽  
Denoising Method ◽  
Detail Coefficient

This paper is a comparative study of image denoising using previously known wavelet transform and new type of wavelet transform, namely, Diversity enhanced discrete wavelet transform. The Discrete Wavelet Transform (DWT) has two parameters: the mother wavelet and the number of iterations. For every noisy image, there is a best pair of parameters for which we get maximum output Peak Signal to Noise Ratio, PSNR. As the denoising algorithms are sensitive to the parameters of the wavelet transform used, in this paper comparison of DEDWT to DWT has been presented. The diversity is enhanced by computing wavelet transforms with different parameters. After the filtering of each detail coefficient, the corresponding wavelet transforms are inverted and the estimated image, having a higher PSNR, is extracted. To benchmark against the best possible denoising method three thresholding techniques have been compared. In this paper we have presented a more practical, implementation oriented work.

scholarly journals Image Denoising Based on Wavelet Transform using Visu Thresholding Technique

2018 ◽  
Vol 3 (4) ◽  
pp. 444-449 ◽  
Author(s):  
Pushpa Koranga ◽  
Garima Singh ◽  
Dikendra Verma ◽  
Shshank Chaube ◽  
Anuj Kumar ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Root Mean Square Error ◽  
Image Denoising ◽  
Environmental Problem ◽  
Signal To Noise Ratio ◽  
Mean Square ◽  
Signal To Noise ◽  
Denoising Method ◽  
Degraded Image

The image often contains noises due to several factors such as a problem in devices or due to an environmental problem etc. Noise is mainly undesired information, which degrades the quality of the picture. Therefore, image denoising method is adopted to remove the noises from the degraded image which in turn improve the quality of the image. In this paper, image denoising has been done by wavelet transform using Visu thresholding techniques for different wavelet families. PSNR (Peak signal to noise ratio) and RMSE (Root Mean Square Error) value is also calculated for different wavelet families.

Sign in / Sign up

Export Citation Format

Share Document