It has been traditional to constrain image processing to linear operations upon the image. This is a realistic limitation of analog processing. In this paper, we find the optimum restoration of a noisy image by the criterion that expectation 〈 〉 be a minimum. Subscript j denotes the spatial frequency ωj at which the unknown object spectrum is to be restored, denotes the optimum restoration by this criterion, and K is any positive number at the user’s discretion. In general, such processing is nonlinear and requires the use of an electronic computer. Processor uses the presence of known, Markov-image statistics to enhance the restoration quality and permits the image-forming phenomenon to obey an arbitrary law Ij = ℒ(τj, , Nj). Here, τj denotes the intrinsic system characteristic (usually the optical transfer function), and Nj represents a noise function. When restored values , j=1, 2, ⋯, are used as inputs to the band-unlimited restoration procedure (derived in a previous paper), the latter is optimized for the presence of noise. The optimum is found to be the root of a finite polynomial. When the particular value K=2 is used, the root is known analytically. Particular restorations are found for the case of additive, independent, gaussian detection noise and a white object region. These restorations are graphically compared with that due to conventional, linear processing.
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