Statistical multirate high-resolution signal reconstruction using the EMD-IT based denoising approach

Abstract

The reconstruction problem of a high-resolution (HR) signal from a set of its noise-corrupted low-resolution (LR) versions is considered. As a part of this problem, a hybrid method that consists of four operation units is proposed. The first unit applies noise reduction based on the empirical mode decomposition interval-thresholding to the noisy LR observations. In the second unit, estimates of zero-interpolated HR signals are obtained by performing up-sampling and then time shifting on each noise reduced LR signal. The third unit combines the zero-interpolated HR signals for attaining one HR signal. To eliminate the ripple effect, finally, median filtering is applied to the resulting reconstructed signal. As compared to the work that employs linear periodically time-varying Wiener filters, the proposed method does not require any correlation information about desired signal and LR observations. The validity of the proposed method is demonstrated by several simulation examples.