Image denoising and enhancement based on adaptive wavelet thresholding and mathematical morphology

Wavelet thresholding is an effective way of image denoising and enhancement. The most important issue in wavelet thresholding is how to find an optimal threshold. In this paper, an adaptive threshold selection technique is proposed and morphological operations to improve the denoised result are discussed. An image denoising and enhancement scheme based on the adaptive wavelet shrinkage and mathematical morphology is described. Compared with some existing denoising methods such as VisuShrinkage, BayesShrinkage, the experimental result shows the proposed method outperforms these techniques in terms of PSNR (Peak Signal to Noise Ratio) and MSE (Mean Square Error).