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A generalized maximum entropy based approach to noisy inverse problems such as the Abel problem, tomography, or deconvolution is discussed and reviewed. Unlike the more traditional regularization approach, in the method discussed here, each unknown parameter (signal and noise) is redefined as a proper probability distribution within a certain pre-specified support. Then, the joint entropies of both, the noise and signal probabilities, are maximized subject to the observed data. We use thisdoi:10.1063/1.1477051 fatcat:vnyxdij5izbopclkpg36mhsapq