Wavelet denoising: Comparative analysis and optimization using machine learning

Abstract

Even after a phenomenal progress in the quality of image denoising algorithms over the years, there is yet a vast scope of improving the standard of denoised images. This paper presents a new methodology for denoising by integrating the wavelet denoising technique with regression boosted trees. Based on ensemble learning by regression boosted trees, an optimal threshold value is obtained. Its denoising performance is better than Stein's unbiased risk estimator-linear expansion of thresholds (SURE-LET) method which is an up to date denoising algorithm. We have also compared its performance with the other current state of art wavelet based denoising algorithms like ProbShrink, and BiShrink on the basis of their Peak Signal to Noise Ratio (PSNR). Simulations and experimentation results demonstrate that PSNR of our proposed method outperforms the other methods. Extension to Dual Tree-Complex Wavelet Transform (DT-CWT) is also presented.