%0 Journal Article %1 IJACSA.2011.020309 %A Sachin D Ruikar, Dharmpal D Doye %D 2011 %J International Journal of Advanced Computer Science and Applications(IJACSA) %K Denoising; Image; Kernel. Noise Signal Transform; Wavelet ratio; to %N 3 %T Wavelet Based Image Denoising Technique %U http://ijacsa.thesai.org/ %V 2 %X This paper proposes different approaches of wavelet based image denoising methods. The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. Wavelet algorithms are useful tool for signal processing such as image compression and denoising. Multi wavelets can be considered as an extension of scalar wavelets. The main aim is to modify the wavelet coefficients in the new basis, the noise can be removed from the data. In this paper, we extend the existing technique and providing a comprehensive evaluation of the proposed method. Results based on different noise, such as Gaussian, Poisson’s, Salt and Pepper, and Speckle performed in this paper. A signal to noise ratio as a measure of the quality of denoising was preferred.

@article{IJACSA.2011.020309, abstract = { This paper proposes different approaches of wavelet based image denoising methods. The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. Wavelet algorithms are useful tool for signal processing such as image compression and denoising. Multi wavelets can be considered as an extension of scalar wavelets. The main aim is to modify the wavelet coefficients in the new basis, the noise can be removed from the data. In this paper, we extend the existing technique and providing a comprehensive evaluation of the proposed method. Results based on different noise, such as Gaussian, Poisson’s, Salt and Pepper, and Speckle performed in this paper. A signal to noise ratio as a measure of the quality of denoising was preferred. }, added-at = {2014-02-21T08:00:08.000+0100}, author = {{Sachin D Ruikar}, Dharmpal D Doye}, biburl = {https://www.bibsonomy.org/bibtex/22044290a0aadd6116d85642f7e68400d/thesaiorg}, interhash = {3ace104a6194c8c19eb8706f0f6293a9}, intrahash = {2044290a0aadd6116d85642f7e68400d}, journal = {International Journal of Advanced Computer Science and Applications(IJACSA)}, keywords = {Denoising; Image; Kernel. Noise Signal Transform; Wavelet ratio; to}, number = 3, timestamp = {2014-02-21T08:00:08.000+0100}, title = {{Wavelet Based Image Denoising Technique}}, url = {http://ijacsa.thesai.org/}, volume = 2, year = 2011 }