Recovering an un-blurred image from a single motion-blurred picture has long been a fundamental research problem. If one assumes that the blur kernel – or point spread function (PSF) – is shift invariant, the problem reduces to that of image deconvolution. Image deconvolution can be further categorized as non-blind and blind. In non-blind deconvolution, the motion blur kernel is assumed to be k...

We derive asymptotic normality of kernel type deconvolution density estimators. In particular we consider deconvolution problems where the known component of the convolution has a symmetric λ-stable distribution, 0 < λ ≤ 2. It turns out that the limit behavior changes if the exponent parameter λ passes the value one, the case of Cauchy deconvolution. AMS classification: primary 62G05; secondary...

The method described here performs blind deconvolution of the beamforming output in the frequency domain. To provide accurate blind deconvolution, sparsity priors are introduced with a smoothed `1/`2 regularization term. As the mean of the noise in the power spectrum domain depends on its variance in the time domain, the proposed method includes a variance estimation step, which allows more rob...

The rapid accumulation of continuous paleomagnetic and rock magnetic records acquired from pass-through measurements on superconducting rock magnetometers (SRM) has greatly contributed to our understanding of the paleomagnetic field and paleo-environment. Pass-through measurements are inevitably smoothed and altered by the convolution effect of SRM sensor response, and deconvolution is needed t...