1. A method of processing a digitized image derived from detector instrumentation to form a reconstructed image comprising the steps of: (S1) obtaining the digitized image from an image measuring system comprising the detector instrumentation, and representing the image as a raw image function g(x,y); (S2) obtaining for the system a point spread function h(x,y); (S3) selecting an analytical form for a reconstructed image entropy distribution S(x,y), a function of statistical measure m(x,y), represented by its image vector [S ij ] wherein the following equivalent forms for a total reconstructed image entropy, S = ∫ Ω ⁢ ⁢ ⅆ x ⁢ ⁢ ⅆ y ⁢ ⁢ S ⁡ ( x , y ) ⁢ x , y ∈ Ω ⁢ ⁢ and ⁢ ⁢ S = ∑ ij ⁢ S ij are used; (S4) selecting a set of Lagrange multipliers λ(x,y) represented by image vector λ=[λ ij ] and constructing a diagonal matrix representation Λ 0 =[λ ij ] diagonal from said image vector components; (S5) calculating a variance statistic σ −2 for normal quadratic statistics and an asymmetric statistic L asym (Λ 0 ) for statistical analysis beyond said normal statistics; (S6) selecting the statistical measure m(x,y) represented by an image vector m=[m ij ]; (S7) producing a reconstructed image function f(x,y) using the following restoration equation wherein an iterative process is repeated until the successive change in f(x,y) is smaller than a user defined tolerance: 
 ∇ S= 2 WD*W −1 Λ 0 σ −2 Λ 0 ( g−WDW −1 f )−∇ L asym (Λ 0 ) further wherein W −1 and W denote Fourier transform and Fourier inverse transform matrix operators respectively, D is a diagonal matrix representation of the point spread function h(x,y), g=[g ij ] and f=[f ij ] are the vector representations of the unprocessed and normalized reconstructed images g(x,y) and f(x,y) respectively and ∇ the gradient operator over the vector field of f represented by the vector [∂/∂f ij ] and * is the complex conjugation operator; and (S8) iteratively repeating steps (S3), (S4), (S5), (S6) and (S7) to determine values for Lagrange multipliers λ(x,y) which optimize the cumulated probability P c of data reconstruction g recon (x,y), represented as an image vector g recon =[g ij recon ] and defined as h(x,y) convoluted with f(x,y), matching g(x,y) further wherein g opt (x,y) represented by image vector g opt =[g ij opt ] is the optimum data reconstruction vector at which values said probability is maximal.