Improved anti-noise attack ability of image encryption algorithm using de-noising technique

Information security is considered as one of the important issues in the information age used to preserve the secret information through out transmissions in practical applications. With regard to image encryption, a lot of schemes related to information security were applied. Such approaches might be categorized into 2 domains; domain frequency and domain spatial. The presented work develops an encryption technique on the basis of conventional watermarking system with the use of singular value decomposition (SVD), discrete cosine transform (DCT), and discrete wavelet transform (DWT) together, the suggested DWT-DCT-SVD method has high robustness in comparison to the other conventional approaches and enhanced approach for having high robustness against Gaussian noise attacks with using denoising approach according to DWT. MSE in addition to the peak signal-to-noise ratio (PSNR) specified the performance measures which are the base of this study’s results, as they are showing that the algorithm utilized in this study has high robustness against Gaussian noise attacks

Underwater Image De-nosing using Discrete Wavelet Transform and Pre-Whitening Filter

Image denoising and improvement are essential processes in many underwater applications. Various scientific studies, including marine science and territorial defence, require underwater exploration. When it occurs underwater, noise power spectral density is inconsistent within a certain range of frequency, and the noise autocorrelation function is not a delta function. Therefore, underwater noise is characterised as coloured noise. In this study, a novel image denoising technique is proposed using discrete wavelet transform with different basis functions and a whitening filter, which converts coloured noise characteristics to white noise prior to the denoising process. Results of the proposed method depend on the following performance measures: peak signal-to-noise ratio (PSNR) and mean squared error. The results of different wavelet bases, such as Debauchies, biorthogonal and symlet, indicate that the denoising process that uses a pre-whitening filter produces more prominent images and better PSNR values than other methods