**6.3 SAR image denoising using Wavelet**

This is the first and lowest level operation to be done on images. The input and the output are both intensity images. The main idea with the preprocessing is to suppress information in the image that is not relevant for its purpose or the following analysis of the image (Subashini &Krishnaveni, 2010). The pre-processing techniques use the fact that neighboring pixels have essentially the same brightness. There are many different pre-processing methods developed for different purposes.Interesting areas of pre-processing for this work is image filtering for noise suppression. Two shrinkage methods are used over here to calculate new pixel values in a local neighborhood. Shrinkage is a well known and appealing denoising technique. On the experiment evaluation, Daubechies wavelet family of orthogonal wavelets is concluded as the appropriate family for shrinkage method as it is defined as a discrete wavelet transform and characterized by a maximal number of vanishing moments for some given support.

Fig. 9. Daubechies wavelet based on level 2 decomposition

Image Denoising Based on Wavelet Analysis for Satellite Imagery 471

Fig. 12. PSNR and MSE values for shrinkage method based on DB Wavelet Family

algorithm and shorten the execution time of the projected approach.

thresholding estimators in removing noise from visual images.

**7. Conclusion and further development** 

Figure 10, 11 shows the evaluation of wavelet families to find the best and Daubechies wavelet is been concluded for wavelet shrinkage denoising. Figure 12 represents the objective evaluation of the shrinkage methods and finally surelet shrinkage is been concluded as the optimal method for denoising. Here an attempt is made to find a superior methodology for denoising than the conventional fixed-form neighborhoods. This approach also determines optimal results by using finest threshold instead of using the suboptimal universal threshold in all bands. It exhibits an excellent performance for ice detection and the experimental result also signifies the same by producing both higher PSNRs and enhanced visual eminence than the former and conventional methods. In future, research will be carried out to reduce the computational load of the proposed image classification

Recently, there has been considerable interest in using the wavelet transform as a powerful tool for recovering SAR images from noisy data. The main reason for the choice of multiscale bases of decompositions is that the statistics of many natural signals, when decomposed in such bases are significantly simplified. When multiplicative contamination is concerned, multiscale methods involve a preprocessing step consisting of a logarithmic transform to separate the noise from the original image. However, thresholding methods have two main drawbacks: i) the choice of the threshold, arguably the most important design parameter, is made in an *ad hoc* manner; and ii) the specific distributions of the signal and noise may not be well matched at different scales. To address these disadvantages, Bayesian theory can be introduced , which outperform classical linear processors and simple

Denoising should not be confused with smoothing. Smoothing removes high frequencies and retains low frequencies whereas denoising attempts to remove whatever noise is present and retain whatever signal is present regardless of the spectral content of the noisy signal.Wavelet shrinkage denoising is denoising by shrinking (i.e., nonlinear soft thresholding) coefficients in the wavelet transform domain. It consists of three steps: 1) a linear forward wavelet transform, 2) a nonlinear shrinkage denoising, and 3) a linear inverse wavelet transform. Because of the nonlinear shrinking of coefficients in the transform domain, this procedure is distinct from those denoising methods that are entirely linear. Moreover, it is considered as a nonparametric method. Thus, it is distinct from parametric

Fig. 10. Peak to Signal noise ratio for wavelet methods

Fig. 11. Mean square error rate for wavelet methods

**Peak Signal to Noise Ratio**

Image1 Image2 Image3

Image1 Image2 Image3 Haar DB Sym Coif Bior Rbior **Wavelet Families** 

**Mean Square Error rate**

Haar DB Sym Coif Bior Rbior **Wavelet Families** 

Fig. 9. Daubechies wavelet based on level 2 decomposition

Fig. 10. Peak to Signal noise ratio for wavelet methods

Fig. 11. Mean square error rate for wavelet methods

**Error rate** 

**Range of Values (db)**

Fig. 12. PSNR and MSE values for shrinkage method based on DB Wavelet Family

Figure 10, 11 shows the evaluation of wavelet families to find the best and Daubechies wavelet is been concluded for wavelet shrinkage denoising. Figure 12 represents the objective evaluation of the shrinkage methods and finally surelet shrinkage is been concluded as the optimal method for denoising. Here an attempt is made to find a superior methodology for denoising than the conventional fixed-form neighborhoods. This approach also determines optimal results by using finest threshold instead of using the suboptimal universal threshold in all bands. It exhibits an excellent performance for ice detection and the experimental result also signifies the same by producing both higher PSNRs and enhanced visual eminence than the former and conventional methods. In future, research will be carried out to reduce the computational load of the proposed image classification algorithm and shorten the execution time of the projected approach.
