Generalized cross validation for multiwavelet shrinkage

Traditional multiwavelet shrinkage denoising techniques require a priori knowledge of noise variance that may not be obtained in some practical situations. By using generalized cross validation (GCV), we propose in this paper a new level-dependent risk estimator for multiwavelet shrinkage that does not require such a priori information. Simulation results verify that the resulted risk estimator gives better indication on threshold selection comparing with the traditional GCV method. Improved denoising performance is then achieved particularly for higher multiplicity multiwavelet shrinkage.