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Experimental Design and Result Analysis

TELKOMNIKA ISSN: 1693-6930  Image Denoising Based on K-means Singular Value Decomposition Jian Ren 1316

5. Experimental Design and Result Analysis

In order to verify the effectiveness and superiority of this algorithm of this paper in the low-signal-to-noise-ratio image, this paper compares the de-noising effect by the algorithm of this paper and the effects by another 2 de-noising algorithms: the image de-noising algorithm based on Symlets wavelet hard threshold and method based on DCT over-complete atom dictionary. The symlets are nearly symmetrical wavelets proposed by Daubechies as modifications to the db family [16]. The properties of the two wavelet families are similar. Here are the wavelet functions as shown in the following Figure 2. Figure 2. Symmetrical wavelets With the Cameraman image as example, this paper conducts experimental simulation and research analysis. The image Cameraman has more smooth regions and abundant detail textures and it also has strong representativeness in de-noising processing. The following is the analysis of the simulation result. Figure 3 is the de-noised images by the foregoing de-noising methods. After being processed by the algorithm of this paper, the edges and textures of Image Cameraman are clear. In the algorithm of this paper and according to the understating of sparse decomposition in the noisy signals, with the increase of noisy components the signal to noise ratio reduces gradually, the useful signal components increasingly reduce, namely that the components with structural property reduce, therefore, in sparse decomposition, the matching atoms to signals become fewer and fewer, the signal representation is more and more sparse and the computation also falls dramatically. In this case, to use K-SVD algorithm to optimize dictionary structure has demonstrated a huge potential in processing low SNR images, suggesting the superiority of the algorithm in this paper. This algorithm can preserve and enhance the image edges and textures and improve the subjective effect and objective quality of the image while removing ringing and blurring effect.  ISSN: 1693-6930 TELKOMNIKA Vol. 13, No. 4, December 2015 : 1312 – 1318 1317 a Noisy image b Denoised image by Symlets wavelet c Denoised image by DCT method d Denoised image by this method Figure 3. Denoised Cameraman images by different denoising methods

6. Conclusion

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