Adaptive edge-preserving denoising by point-wise wavelet basis selection

Wavelet transforms found widespread application in signal denoising. Many adaptive algorithms were proposed to improve their performance, especially about edges in a signal. In this study, the authors propose a novel denoising method based on adaptive edge-preserving lifting scheme - intersection of confidence intervals-edge preserving lifting scheme (ICI-EPL). By incorporating the statistical method of intersection of confidence intervals rule into the lifting scheme, the authors are able to select the most appropriate wavelet on a point-by-point basis. The resulting transform adapts very well to local signal properties and significantly improves denoising performance. Simulations on various signal classes show that the ICI-EPL in most cases easily outperforms other considered transforms, with the greatest improvement being about edges in a signal. Achieved results bring confidence that the ICI-EPL can be used to improve performance in a variety of denoising applications.