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## Introduction

Consider an image characterized by its intensity distribution I(x,y), corresponding to the observation of an object O(x,y) through an optical system. If the imaging system is linear and shift-invariant, the relation between the object and the image in the same coordinate frame is a convolution:

 I(x,y)= O(x,y) * P(x,y) + N(x,y) (14.99)

P(x,y) is the point spread function (PSF) of the imaging system, and N(x,y) is an additive noise. In Fourier space we have:
 (14.100)

We want to determine O(x,y) knowing I(x,y) and P(x,y). This inverse problem has led to a large amount of work, the main difficulties being the existence of: (i) a cut-off frequency of the PSF, and (ii) an intensity noise (see for example [6]).

Equation 14.99 is always an ill-posed problem. This means that there is not a unique least-squares solution of minimal norm and a regularization is necessary.

The best restoration algorithms are generally iterative [24]. Van Cittert [41] proposed the following iteration:

 (14.101)

where is a converging parameter generally taken as 1. In this equation, the object distribution is modified by adding a term proportional to the residual. But this algorithm diverges when we have noise [12]. Another iterative algorithm is provided by the minimization of the norm [21] and leads to:

 (14.102)

where Ps(x,y)=P(-x,-y).

Tikhonov's regularization [40] consists of minimizing the term:

 (14.103)

where H corresponds to a high-pass filter. This criterion contains two terms; the first one, , expresses fidelity to the data I(x,y) and the second one, , smoothness of the restored image. is the regularization parameter and represents the trade-off between fidelity to the data and the restored image smoothness. Finding the optimal value necessitates use of numeric techniques such as Cross-Validation [15] [14].

This method works well, but it is relatively long and produces smoothed images. This second point can be a real problem when we seek compact structures as is the case in astronomical imaging. An iterative approach for computing maximum likelihood estimates may be used. The Lucy method [#lucy<#15258,#katsaggelos<#15259,#adorf<#15260] uses such an iterative approach:

 (14.104)

and
 (14.105)

where P* is the conjugate of the PSF.

Next: Regularization in the wavelet Up: Deconvolution Previous: Deconvolution
Petra Nass
1999-06-15