English

Abstract

In the past two decades, many speckle reduction techniques have been developed for removing speckle and retaining edge details in Synthetic Aperture Radar(SAR) images. Most of the standard algorithms use a defined filter window to estimate the local noise variance of a speckle image and perform the individual unique filtering process.The primary goal of speckle reduction is to remove the speckle without losing much detail contained in an image. To achieve this goal, we make use of a mathematical function known as the wavelet transform and apply multiresolution analysis to localize an image into different frequency components or useful sub-bands and effectively reduce the speckle in the sub-bands according to the local statistics within the bands. The main advantage of the wavelet transform is that the image fidelity after reconstruction is visually lossless.In this project, we will study and investigate the application of using the Daubechies wavelet with denoising techniques to remove speckle in SAR images. We combine the wavelet shrinkage denoising techniques with different wavelet basis and decomposition levels on the individual sub-bands to achieve the best acceptable speckle reduction while maintaining the fidelity of the image. The analysis of the statistical results will be calculated using matlab to demonstrate the advantages and disadvantages of using complex wavelet shrinking techniques over standard speckle filters.