The bidimensional empirical mode decomposition with 2D-DWT for gaussian image denoising

This paper presents a new adaptive approach for image denoising with Gaussian noise based on a combination of the Bidimensional Empirical Mode Decomposition (BEMD) and the the discrete wavelet transforms (DWT). The BEMD is an auto-adaptive method for the analysis of nonlinear or non-stationary signals and images. The input image is decomposed into several modes called Intrinsic Mode Functions (IMFs), which show new characteristics of the images. In this paper, we propose to apply the BEMD approach in the image denoising domain by using the first IMF to reduce the Gaussian noise in blurred images. After that, we combine the BEMD with the DWT to improve the BEMD denoising method. Finally, we show the influence of the number of IMFs filtered with the DWT on the visual quality in term of PSNR of the denoised image.