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Vol.3 , No. 4, Publication Date: Aug. 18, 2016, Page: 19-25
. Then, the entropy values of regions are calculated and considered as the estimation of noise complexity in each region. According to the segments’ entropy values, the regions are divided in two clusters; one cluster consists of regions with lower entropy than the mean of entropy values and other cluster is regions with the higher entropy values. Different wavelets are selected for denoising of clusters because the performance of a wavelet in image denoising is different in relation to the level of noise. The regions with higher entropy can be denoised by Db4, which is the wavelet that has better performance when noise level is high, and a common wavelet such as Db2 can be used for denoising in lower entropy regions. Finally, the denoised image reconstructed by composition of denoised regions. The simulation results show that the proposed method produces better denoised image than using of one wavelet in an image.&picture=http://www.aascit.org/aascit/decorators/img/journal/915.jpg)
| [1] | Abdolreza Dehghani Tafti, Department of Electrical Engineering, Islamic Azad University Karaj Branch, Karaj, Iran. |
| [2] | Serajeddin Ebrahimian Hadi Kiashari, Department of Mechatronics Engineering, K. N. Toosi University of Technology, Tehran, Iran. |
In this paper, a new method for adaptive wavelet image denoising is proposed. In the proposed method, the noisy image is segmented to the homogenus color-texture regions by JSEG (J-segmentation). Then, the entropy values of regions are calculated and considered as the estimation of noise complexity in each region. According to the segments’ entropy values, the regions are divided in two clusters; one cluster consists of regions with lower entropy than the mean of entropy values and other cluster is regions with the higher entropy values. Different wavelets are selected for denoising of clusters because the performance of a wavelet in image denoising is different in relation to the level of noise. The regions with higher entropy can be denoised by Db4, which is the wavelet that has better performance when noise level is high, and a common wavelet such as Db2 can be used for denoising in lower entropy regions. Finally, the denoised image reconstructed by composition of denoised regions. The simulation results show that the proposed method produces better denoised image than using of one wavelet in an image.
Keywords
Adaptive Image Denoising, Wavelet Denoising, Entropy, JSEG Method
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