Wavelet denoising by recursive cycle spinning

Coupling the periodic time-invariance of the wavelet transform with the view of thresholding as a projection yields a simple, recursive, wavelet-based technique for denoising signals. Estimating a signal from a noise-corrupted observation is a fundamental problem of signal processing which has been addressed via many techniques. Previously, Coifman and Donoho introduced cycle spinning, a technique estimating the true signal as the linear average of individual estimates derived from wavelet-thresholded translated versions of the noisy signal. Here, it is demonstrated that such an average can be dramatically improved upon. The proposed algorithm recursively "cycle spins" by repeatedly translating and denoising the input via basic wavelet denoising and then translating back; at each iteration, the output of the previous iteration is used as input. Exploiting the convergence properties of projections, the proposed algorithm can be regarded as a sequence of denoising projections ...

• Type: Conference
• Year: 2002
• Where: ICIP
• Authors: Alyson K. Fletcher, Vivek K. Goyal, Kannan Ramchandran
• Keywords: Basic Wavelet Thresholding, ICIP 2002, Image Processing, Original Noisy Signal, True Signal