next up previous
Next: Parametric approach and Cramer-Rao Up: Superresolution algorithms in discrete Previous: Introduction

Non-parametric approach and Shannon's superresolution limit

The non-parametric approach, also named the unfolding problem, is applied when the problem parametrization is unknown. Mathematically the problem is reduced to the solution of the following integral equation of the first kind

  eqnarray15

where the unknown function is f(y);SPMgt;0 and so-called instrumental function K(x) is, in general, determined by a specific of the physical problem. Our contribution to the problem is the explicit including to the kernel K(r) the effect of the information loss due to the histograming process. To take into account the finite size of our histogram bin we substitute the kernel K(x) in (1) for the convolution tex2html_wrap_inline195 with the characteristic function of the interval tex2html_wrap_inline197 , i.e. h(x)=1 for tex2html_wrap_inline201 and 0 otherwise. Although for continuous kernels this problem is ill-posed (see for example [1]), however as it was there proved, when one looks for a solution on a compact set the problem can be solved for a sufficiently wide class of kernels including the convolution tex2html_wrap_inline205 . Due to always presenting noise there is the resolution limit for close signals, which is non-improved of principle. That follows from the well-known Shannon's theory on the maximum speed of the data transmission via the channel having a noise. Such the limiting superresolution factor is (see [2])

equation22

Here tex2html_wrap_inline207 is the signal energy: tex2html_wrap_inline209 and tex2html_wrap_inline211 is the noise energy: tex2html_wrap_inline213 where n is the number of experimental data points, and tex2html_wrap_inline217 is the variance of input noise. If the signal-to-noise ratio is expressed in decibels tex2html_wrap_inline219 the approximate expression for the superresolution limit is tex2html_wrap_inline221 .

In the paper [3] the computer program package RECOVERY was described, which can reach the Shannon superresolution limit. The programs from the RECOVERY package are based on the maximum likelihood principle and they look for maximum of the likelihood function in the many-dimensional set of solutions, which is always a compact set. The RECOVERY code is accessible from the CPC Program Library. The results of non-parametric approach described in this paper was obtained by use the modified DCONV program from the RECOVERY package. Further we refer to non-parametric method as to NP.


next up previous
Next: Parametric approach and Cramer-Rao Up: Superresolution algorithms in discrete Previous: Introduction

Alexander S. Semenov
Wed Jul 2 11:24:36 MSD 1997