Underwater Image De-nosing using Discrete Wavelet Transform and Pre-Whitening Filter - Mohanad Najm Abdulwahed; Ali Kamil Ahmed (Universitas Ahmad Dahlan, 2018)
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.