scholarly journals Removal of Gaussian Noise from Degraded Images in Wavelet Domain

2006 ◽  
Vol 126 (11) ◽  
pp. 1351-1358 ◽  
Author(s):  
Yeqiu Li ◽  
Jianming Lu ◽  
Ling Wang ◽  
Takakshi Yahagi
2008 ◽  
Vol 91 (1) ◽  
pp. 11-18
Author(s):  
Yeqiu Li ◽  
Jianming Lu ◽  
Ling Wang ◽  
Takakshi Yahagi

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Julio Ramírez-Pacheco ◽  
Homero Toral-Cruz ◽  
Luis Rizo-Domínguez ◽  
Joaquin Cortez-Gonzalez

This paper defines the generalized wavelet Fisher information of parameterq. This information measure is obtained by generalizing the time-domain definition of Fisher’s information of Furuichi to the wavelet domain and allows to quantify smoothness and correlation, among other signals characteristics. Closed-form expressions of generalized wavelet Fisher information for1/fαsignals are determined and a detailed discussion of their properties, characteristics and their relationship with waveletq-Fisher information are given. Information planes of1/fsignals Fisher information are obtained and, based on these, potential applications are highlighted. Finally, generalized wavelet Fisher information is applied to the problem of detecting and locating weak structural breaks in stationary1/fsignals, particularly for fractional Gaussian noise series. It is shown that by using a joint Fisher/F-Statistic procedure, significant improvements in time and accuracy are achieved in comparison with the sole application of theF-statistic.


2012 ◽  
Vol 21 (3) ◽  
pp. 039802-1
Author(s):  
Jan Švihlík ◽  
Karel Fliegel ◽  
Jaromír Kukal ◽  
Eva Jerhotová ◽  
Petr Páta ◽  
...  

2018 ◽  
Vol 7 (3.34) ◽  
pp. 327
Author(s):  
K Sumathi ◽  
Ch Hima Bindu

In this paper, the proposed method is implemented for removal of salt & pepper and Gaussian noise of black & white & color images toacquire the quality output. In this work initially wavelet coefficients are extracted for noisy images. Later apply denoise filteringtechnique on the high transform sub bands of noisy images (either color/ B & W) using new laplacian filters with 4 directions. Finallythreshold of an image is generated to extract denoisy coefficients. At last inverse of above subband coefficients can give denoise imagefor further processing. The proposed method is verified against various B & W/color images and it gives a better PSNR (Peak Signal toNoise Ratio) & MI (Mutual Information). These values are compared with different noise densities and analyzed visually.


2017 ◽  
Vol 17 (01) ◽  
pp. 1750003 ◽  
Author(s):  
P. Kittisuwan

Gaussian noise is an important problem in computer vision. The novel methods that become popular in recent years for Gaussian noise reduction are Bayesian techniques in wavelet domain. In wavelet domain, the Bayesian techniques require a prior distribution of wavelet coefficients. In general case, the wavelet coefficients might be better modeled by non-Gaussian density such as Laplacian, two-sided gamma, and Pearson type VII densities. However, statistical analysis of textural image is Gaussian model. So, we require flexible model between non-Gaussian and Gaussian models. Indeed, Gumbel density is a suitable model. So, we present new Bayesian estimator for Gumbel random vectors in AWGN (additive white Gaussian noise). The proposed method is applied to dual-tree complex wavelet transform (DT-CWT) as well as orthogonal discrete wavelet transform (DWT). The simulation results show that our proposed methods outperform the state-of-the-art methods qualitatively and quantitatively.


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