A crucial step in image restoration involves deconvolving the true object from noisy and often poorly sampled image data. Deconvolution under these conditions represents an ill-posed inversion problem, in that no unique computationally stable solution exists. We propose a statistical information based approach to regularize the deconvolution process. Using Shannon Information, one monitors the information about the object that is processed during the deconvolution in order to obtain an optimal stopping criterion and hence the``best' solution to the inversion problem. The optimal stopping criterion is based on how Shannon Information changes in the spatial frequency domain as the deconvolution proceeds. We present results for the Maximum Entropy Method (MEM) and Richardson-Lucy (RL) non-linear deconvolution techniques.