subsubsection example search window size, the metricsubsubsection example search window size, the metric

subsubsection {Bm3d}

cite{2}In some holography denoising recent methods, the pixels of image is classified into heterogeneous regions and then different methods and parameters are used to denoising these regions. In the pixels belonging to each region, the noise distribution is supposed similar and the type and parameters of denoising method keep constant. This type of filtering, that in fact, is a dynamic and semantic denoising process is known as block matching. The main challenge with this type of filtering is finding heterogeneous regions. Many unknown steps are affect this process, for example search window size, the metric for measuring similarity in blocks, the similarity threshold, etc… Once the similar blocks are obtained, blocks belonging to similar regions are clustered, the noise parameters of these regions estimated locally and each region filtered through local filtering.

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subsubsection {Spadadh}

cite{5} Nonlocally centralized sparse representation is an effective approach for estimating original image from a noisy image. In the first step of this method, sparse coefficients from all regions (including regions segmented) is extracted.  The main challenge of this method is to extract sparse coefficients more accurate. After this step the image is classified into different types according to the statistical characteristics of sparse coefficients. For each type of regions, similar to block matching or NLM, an appropriate denoising method is selected. window size or segmentation method, the form of sparse coefficients, similarity or distance measure that used for finding clusters are some of other challenges in this type of denoising.

subsubsection {Lee}

Synthetic Aperture Radar filtering is so similar to holography images. If the noise model is supposed multiplicative in SAR or holography images, many of denoising methods is inefficient.  Lee proposed an adaptive filter cite{6} for solving this problem. The Lee filter can be described by egin{equation}


d(x,y) = alpha s(x,y) + (1 – alpha {mu _s})                                 (1)

In Lee filter, Parameter a = 1 – C_b^2/C_s^2 depends on the ratio of the squares of the local variation coefficients of the image, Cs, and noise, Cb. This local variation coefficient is defined as the ratio of the variance between the mean square of the considered pixel values. In a region with homogeneous(constant) values, the local variation coefficient is low and the filter does not modify pixel value and vice versa. Lee filter uses the minimum mean square error filtering criterion to carry out the despeckling (such as wiener filter that is optimal in gaussian process)

subsubsection {Wavelet}

Wavelet denoising is a very powerful non-linear technique, which operates in time-frequency domain. In its most basic form, each coefficient is compared with threshold, if the coefficient is smaller than threshold, set to zero; otherwise it is kept or modified. The main parameters of this method is its wavelet function, decomposition levels and the form of finding threshold. If the original image corrupted by Gaussian noise, threshold is found by a Bayesian technique using probabilistic model of the image wavelet coefficients that are dependent on generalized Gaussian distribution. In the simple wavelet denoising method the noise considers an additive noise. When the noise is multiplicative, additive signal and noise are obtained by computing the logarithm. This was studied in the case of speckle noise processing in SAR imagescite{8}.