Algorithm for Speckle Reduction and Image Enhancement in SAR Images Using Wavelet Transforms
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 1
Abstract
Image Denoising and enhancement plays key role in the SAR imagery analysis. Detection of features is important and this can be done by image enhancement. The aim is to improve visibility of the low contrast features and suppressing the speckle. Every image has locally varying statistics, and different edges in it. Wavelet transform gives a superior quality of speckle reduction due to multi resolution aspects. SAR is a high resolution imaging radar. It generates images independent of weather and time conditions. It can penetrate through earth to some depth and useful for agriculture, forestry and hydrology ,etc. SAR images are inevitably accompanied by Speckle due to the coherent nature of the imaging system. The speckle reduces detectability of image features. It is essential to remove speckle for proper identification of features and image analysis. Any feature in ribbon like shape like roads runways or edge features require speckle removal for analysis. The effect of speckle can be reduced in two methods. First, in the processing of image during image formation and second using image filtering techniques. The second method suppresses speckle noise using filters like a Lee filter, Kuan/ Nathan filter, etc. In speckled radar images, filtering must achieve a tradeoff between smoothing of homogeneous area and edge and texture preservation. In this work processing has been carried out in wavelet domain. Donoho and Johnstone pioneered the theoretical formalization of filtering additive i.i.d. Gaussian noise (of zero mean and standard deviations) via thresholding wavelet coefficients. A wavelet coefficient is compared to a given threshold and is set to zero if its magnitude is less than the threshold; otherwise, it is kept or modified (depending upon the thresholding rule). The threshold acts as an oracle, which distinguishes between the insignificant coefficients likely due to noise, and the significant coefficients consisting of important signal structures. For image denoising, the threshold choice proposed by Dohono yield overly smoothed images as the threshold choice of s 2logM (called the universal threshold), can be unwarranted large due to its dependence on the number of samples, M, which is more than 105 for a typical test image of size 512 x 512. This thresholding mechanism also requires the knowledge of the amount of noise present in the image. Wavelet transform performs a hierarchical decomposition of the signal space into a nested sequence of approximation spaces by translations and dilatations of one mother wavelet function. The statistics of SAR image was extensively studied in Godman 1976, and concluded that SAR intensity is a ultiplicative noise. Logarithmic transformation of image speckle is approximately Gaussian noise. The image compression and denoising can be done using wavelet based speckle noise removal approaches.
Authors and Affiliations
Dr. N S S R Murthy, Prof. I V Murali krishna
Keywords
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