We extend deconvolution in a periodic setting to deal with functional data. The resulting functional deconvolution model can be viewed as a generalization of a multitude of inverse problems in mathematical physics where one needs to recover initial or boundary conditions on the basis of observations from a noisy solution of a partial differential equation. In the case when it is observed at a f...

Spontaneous postsynaptic currents (PSCs) provide key information about the mechanisms of synaptic transmission and the activity modes of neuronal networks. However, detecting spontaneous PSCs in vitro and in vivo has been challenging, because of the small amplitude, the variable kinetics, and the undefined time of generation of these events. Here, we describe a, to our knowledge, new method for...

In this Diploma Thesis we will present some methods to improve the quality of a given pictures. In particular blind deconvolution will be applied to deblurr the images. The deconvolution tries to invert the blurring of an image that is modeled by the convolution g = f ∗ h. Blind deconvolution tries to do this without knowledge of the point spread function that blurred the image. In the Thesis f...

A new `1 optimal deconvolution filter design approach for systems with uncertain (or unknown)-but-bounded inputs and external noises is proposed. The purpose of this deconvolution filter is to minimize the peak gain from the input signal and noise to the error by the viewpoint of the time domain. The solution consists of two steps. In the first step, the `1 norm minimization problem is transfer...

We extend deconvolution in a periodic setting to deal with functional data. The resulting functional deconvolution model can be viewed as a generalization of a multitude of inverse problems in mathematical physics where one needs to recover initial or boundary conditions on the basis of observations from a noisy solution of a partial differential equation. In the case when it is observed at a f...

In the communication field, during transmission, a source signal undergoes a convolutive distortion between its symbols and the channel impulse response. This distortion is referred to as Intersymbol Interference (ISI) and can be reduced significantly by applying a blind adaptive deconvolution process (blind adaptive equalizer) on the distorted received symbols. But, since the entire blind deco...

BACKGROUND An unknown input function can be determined by deconvolution using the systemic bolus input function (r) determined using an experimental input of duration ranging from a few seconds to many minutes. The quantitative relation between the duration of the input and the accuracy of r is unknown. Although a large number of deconvolution procedures have been described, these routines are ...

Colour deconvolution is a method used in diagnostic brightfield microscopy to transform colour images of multiple stained biological samples into images representing the stain concentrations. It is applied by decomposing the absorbance values of stain mixtures into absorbance values of single stains. The method assumes a linear relation between stain concentration and absorbance, which is only ...

In this paper we present a general framework of the state space approach for blind deconvolution. First, we review the current state of the art of blind deconvolution using statespace models, then give a new insight into blind deconvolution in the state-space framework. The cost functions for blind deconvolution are discussed and adaptive learning algorithms for updating external parameters are...

Introduction With the advances in fast MR imaging techniques, it is possible to monitor the traversal of a MR contrast agent within tissues at short time intervals (of the order of a few seconds or less), thereby allowing the possibility to study blood flow at the tissue level. Dynamic contrastenhanced MRI (DCE MRI) is emerging as a promising approach for in vivo assessment of tissue microcircu...