The use of images, text and video has become inevitable in today s rapidly advancing and growing digital world. It has been greatly assisted by the contributions of information technology in bringing about state of the art high speed and secure transmission and reception systems. Content transmission through e-mails, hard media like hard disk drives (HDD),compact discs (CD), digital video discs (DVD) etc., have made things simpler, safer and faster. Digital images can be captured easily with scanners, digital cameras and camcorders, and transmitted easily over the Internet.With the advent of tele based services, numerous merits have been reported like cutting down of transportation time of patient to meet the doctor or hospital, cutting down of cost due to internet based transmission and reception and also efficient patient monitoring and treatment due to systematic and methodological secured storage of the patient information in the hospital s data base for future access. "Watermarking" is a process of hiding digital information in original data, to increase robustness and security using the different technique like Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD).Methods till date result in good security but they are not robust enough against different attacks. The aim of this research work is to develop a robust and secure watermarking scheme against various sorts of attacks. The robustness and security is increased by combining DWT and SVD. Accordingly an efficient scheme is developed that is having better MSE and PSNR against a wide range of attacks. Keywords: Digital Wavelet Transform, Singular Value Decomposition, Digital Watermarking, Mean Square Error, Peak Signal to Noise Ratio, Noise Attacks

Recently, compressive sampling has expected noteworthy attention as an promising technique for rapid volumetric image techniques . This paper investigated optical coherence tomography (OCT) image acquisition using compressive sampling techniques .Previous researches used the multidimensional wavelet transform as the domain of sparsification for recovering OCT images in volumes. In this paper we analyzed and compared the potential and efficiency of other image transforms to recreate the similar OCT image. The two quantitative measures, the mean square errorand the structural similarity index, were applied to compare the quality of the reconstructed volumetric images. We observed that fast fourier transformation and wavelet transformation both are able to reconstruct OCT image volumes for the orthogonal sparse sampling masks used in this report, but with different merits.