Adaptive Thresholding Denoising Algorithm Based on Cross-Validation
Wenqing Huang, Yuxing Dai · 2006
In this paper, a novel wave let-based adaptive thresholding de-noising algorithm is proposed. By using a modified twofold Cross-validation, a noise-corrupted signal is divided into two parts: one for estimating, the other one acts as a reference signal, and they make it possible to s earch for the optimal threshold using steepest gradient method. The numerical results indicate that the proposed optimal-threshold-based denoising algorithm outperforms the standard wavelet shrinkage methods, like Donoho's VisuShrink and SureShrink, in MSE sense. The proposed algorithm does not need any a priori information of the noise-distorted signal, and its convergence speed is high. It fits to real-time signal processing.