Security Based Image Processing Using Splines and Wavelets

Abstract

The main goal of the thesis is to show a novel use of the wavelet transform and singular value decomposition for enhancing the security of images through watermarking. The properties of wavelets make them special in that they have a good time and frequency localization which make them ideal for the processing of non-stationary signals like the images. The traditional Fourier transform only provides the spectral information of a signal and thus it is not suitable for the analysis of non-stationary signals. The lifting wavelet transform (LWT) is a recent approach to wavelet transform and singular value decomposition (SVD) is a valuable transform technique for robust digital watermarking. While LWT allows generating an infinite number of discrete biorthogonal wavelets starting from an initial one, singular values (SV) allow us to make changes in an image without affecting the image quality much. This thesis presents an approach which tries to amalgamate the features of these two transforms to achieve a hybrid and robust digital image watermarking techniques. Certain performance metrics are used to test the robustness of the method against common image processing attacks. newlineNext we show the image processing algorithms for de-noising, reconstruction and watermarking, using wavelets and splines that can be applied successfully to enhance noisy multidimensional image data sets. Noise removal or de-noising is an important task in image processing used to recover a signal that has been corrupted by noise. Random noise that is present in images is generated by electronic components in the instrumentation. The thesis present an Intelligent Recursive Algorithm (IRA) based on lifting filter that can efficiently remove noise. The algorithm does not need any threshold parameters unlike the algorithms developed so far using PSM and median based filters.