Abstract: Most simple nonlinear thresholding rules for
wavelet- based denoising assume that the wavelet coefficients are independent. However, wavelet coefficients of natural images have significant dependencies. This paper attempts to give a recipe for selecting one of the popular image-denoising algorithms based
on VisuShrink, SureShrink, OracleShrink, BayesShrink and BiShrink and also this paper compares different Bivariate models used for image denoising applications. The first part of the paper
compares different Shrinkage functions used for image-denoising.
The second part of the paper compares different bivariate models
and the third part of this paper uses the Bivariate model with modified marginal variance which is based on Laplacian assumption. This paper gives an experimental comparison on six 512x512 commonly used images, Lenna, Barbara, Goldhill,
Clown, Boat and Stonehenge. The following noise powers 25dB,26dB, 27dB, 28dB and 29dB are added to the six standard images and the corresponding Peak Signal to Noise Ratio (PSNR) values
are calculated for each noise level.
Abstract: Random Oracle Model (ROM) is an effective method
for measuring the practical security of cryptograph. In this paper, we
try to use it into information hiding system (IHS). Because IHS has its
own properties, the ROM must be modified if it is used into IHS.
Firstly, we fully discuss why and how to modify each part of ROM
respectively. The main changes include: 1) Divide the attacks that IHS
may be suffered into two phases and divide the attacks of each phase
into several kinds. 2) Distinguish Oracles and Black-boxes clearly. 3)
Define Oracle and four Black-boxes that IHS used. 4) Propose the
formalized adversary model. And 5) Give the definition of judge.
Secondly, based on ROM of IHS, the security against known original
cover attack (KOCA-KOCA-security) is defined. Then, we give an
actual information hiding scheme and prove that it is
KOCA-KOCA-secure. Finally, we conclude the paper and propose the
open problems of further research.
Abstract: Due to the non- intuitive nature of Quantum
algorithms, it becomes difficult for a classically trained person to
efficiently construct new ones. So rather than designing new
algorithms manually, lately, Genetic algorithms (GA) are being
implemented for this purpose. GA is a technique to automatically
solve a problem using principles of Darwinian evolution. This has
been implemented to explore the possibility of evolving an n-qubit
circuit when the circuit matrix has been provided using a set of
single, two and three qubit gates. Using a variable length population
and universal stochastic selection procedure, a number of possible
solution circuits, with different number of gates can be obtained for
the same input matrix during different runs of GA. The given
algorithm has also been successfully implemented to obtain two and
three qubit Boolean circuits using Quantum gates. The results
demonstrate the effectiveness of the GA procedure even when the
search spaces are large.