Incomplete system models can cause image restoration failures

It is unfortunate but true that incomplete imaging system models are commonly used for simulation-based image restoration studies. As an illustration of this, a common-but- incomplete continuous-input/continuous-output (c/c) system model is used in this paper to produce some simulated restorations. In this way, it is demonstrated that when image formation blur is the only significant source of image degradation, then conventional c/c model-based restoration can successfully sharpen blurred images, even when the blurring is excessive. If however, at least a small amount of additive random noise is also a source of image degradation (as is always the case in practice), then conventional c/c model-based restoration will produce satisfactory results, if at all, only if the restoration filter is consistent with a more comprehensive system model that accounts for the presence of this noise. Moreover, if sampling and reconstruction are part of the imaging process then conventional c/c model-based restoration can fail to produce satisfactory results; this is true even if there is no additive random noise. In this way, it is demonstrated that a c/c model is not a correct sampled imaging system model; instead a more comprehensive continuous-input/discrete- processing/continuous-output model should be used as the basis for simulation-based restoration studies.