The unconstrained design of optimal digital window-based filters from sample signals is very difficult because of the inability to obtain enough data to make good estimates of the filter parameters. This paper studies the estimation problem by windowing in the range, as well as in the domain. At each point, the signal is viewed through an aperture, which is the product between a domain window and a gray-scale range window. Signal values outside the aperture are project to the limit values inside the aperture. Experiments show that aperture filters can outperform linear filters for deblurring, especially in the restoration of edges. A sampling of the many experiments carried out to study the effects of aperture filters on deblurring is provided.