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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:
$\displaystyle \hat I(u,v)= \hat O(u,v) \hat P(u,v) + \hat N(u,v)$     (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 $\parallel I(x,y) - P(x,y) * O(x,y) \parallel^2$ and a regularization is necessary.

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

$\displaystyle O^{(n+1)} (x,y) = O^{(n)} (x,y) + \alpha(I(x,y) - P(x,y)* O^{(n)} (x,y))$     (14.101)

where $\alpha$ 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 $\parallel I(x,y) - P(x,y)* O(x,y)
\parallel^2$[21] and leads to:
$\displaystyle O^{(n+1)} (x,y) = O^{(n)} (x,y) + \alpha P_s(x,y) * [I(x,y) - P(x,y) *
O^{(n)} (x,y)]$     (14.102)

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

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

$\displaystyle \parallel I(x,y) - P(x,y)* O(x,y) \parallel^2 + \lambda \parallel H *
O\parallel^2$     (14.103)

where H corresponds to a high-pass filter. This criterion contains two terms; the first one, $\parallel I(x,y) - P(x,y)* O(x,y) \parallel^2$, expresses fidelity to the data I(x,y) and the second one, $\lambda \parallel H * O\parallel^2$, smoothness of the restored image. $\lambda$ is the regularization parameter and represents the trade-off between fidelity to the data and the restored image smoothness. Finding the optimal value $\lambda$ 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:

$\displaystyle O^{(n+1)} = O^{(n)} [ \frac{I}{I^{(n)}} \ast P^* ]$     (14.104)

$\displaystyle I^{(n)} = P \ast O^{(n)}$     (14.105)

where P* is the conjugate of the PSF.

next up previous contents
Next: Regularization in the wavelet Up: Deconvolution Previous: Deconvolution
Petra Nass