Local histogram filtering utilizing feature selective templates consists of ordering the elements of the subimage histogram contained in the support of a nine element square template translating over the image, rather than ordering the subimage intensity values, as in standard order statistic filtering. Subsequently, the ordered local histogram is constructed from the nonzero subimage histogram elements arranged in descending order. If the subimage value contained in the center of the template support is equivalent to the most frequently occurring subimage intensity, corresponding to the 0th order statistic of the ordered local histogram, the central subimage value is preserved. Alternately, if the central subimage value differs from the most frequency subimage intensity, sequential morphological hit-miss transformations are performed on the subimage employing a subset of 36 feature selective templates specified according to the ordered local histogram element values. The feature selective template hit-miss transformations implement heuristic image smoothness criteria, determining if the central subimage pixel is a component of a valid intensity pattern that is preserved. An unsuccessful hit-miss transformation indicates an invalid intensity pattern, resulting in the modification of the central subimage value to the subimage value corresponding to the 0th order statistic of the ordered local histogram. Consequently, in simulated images consisting of linear, rectangular, circular, and spiral geometric patterns corrupted with uniformly distributed impulsive random noise, the resulting filtered images possess mean absolute errors between 2.0 and 3.9 times less than those of images employing median filtering.