Wavelet-Domain Regularized Deconvolution for ILL-Conditioned Systems

by 0. In the discrete Fourier transform (DFT) domain, We propose a hybrid approach to wavelet-based image deconvolution that comprises Fourier-domain system inversion followed by wavelet-domain noise suppression. In contrast to conventional wavelet-based deconvolution approaches, the algorithm employs a regularized inverse filter, which allows it to operate even when the system is non-invertible. Using a mean-square-error metric, we strike an optimal balance between Fourier-domain regularization that is matched to the system and wavelet-domain regularization that is matched to the signal. Theoretical analysis reveals that the optimal balance is determined by economics of the input signal wavelet representation and the operator structure. The resultant algorithm is fast, O(N1og: N ) where N denotes the number of samples, and is well-suited to data with spatially-localized phenomena such as edges. In addition to enjoyingasymptotically near-optimal rates of error decay for some systems, t...

• Type: Conference
• Year: 1999
• Where: ICIP
• Authors: Ramesh Neelamani, Hyeokho Choi, Richard G. Baraniuk
• Keywords: Conventional Wavelet-based Deconvolution, ICIP 1999, Image Processing, Wavelet-based Deconvolution, Wavelet-based Image Deconvolution