Neelamani, RameshChoi, HyeokhoBaraniuk, Richard G.2007-10-312007-10-311999-03-011999-03-01R. Neelamani, H. Choi and R. G. Baraniuk, "Wavelet-based Deconvolution for Ill-conditioned Systems," vol. 6, 1999.https://hdl.handle.net/1911/20135Conference PaperIn this paper, we propose a new approach to wavelet-based deconvolution. Roughly speaking, the algorithm comprises Fourier-domain system inversion followed by wavelet-domain noise suppression. Our approach subsumes a number of other wavelet-based deconvolution methods. In contrast to other wavelet-based approaches, however, we employ a regularized inverse filter, which allows the algorithm to operate even when the inverse system is ill-conditioned or non-invertible. Using a mean-square-error metric, we strike an optimal balance between Fourier-domain and wavelet-domain regularization. The result is a fast deconvolution algorithm ideally suited to signals and images with edges and other singularities. In simulations with real data, the algorithm outperforms the LTI Wiener filter and other wavelet-based deconvolution algorithms in terms of both visual quality and MSE performance.engwavelet-based deconvolutionFourier-domain systemLTI Wiener filterMSE performancewavelet-domain regularizationImage Processing and Pattern analysisWavelet based Signal/Image ProcessingMultiscale MethodsConference paperwavelet-based deconvolutionFourier-domain systemLTI Wiener filterMSE performancewavelet-domain regularizationhttp://dx.doi.org/10.1109/ICASSP.1999.757532