Regularity is a ubiquitous feature of the visual world. We demonstrate that regularity is an adaptable visual dimension: The perceived regularity of a pattern is reduced following adaptation to a pattern with a similar or greater degree of regularity. Stimuli consisted of 7×7 element arrays arranged on square grids presented in a circular aperture. The position of each element was randomly jittered from its baseline position by an amount that determined its degree of irregularity. The elements of the pattern consisted of dark Gaussian blobs (GBs), difference of Gaussians (DOGs), or random binary patterns (RBPs). Observers adapted for 60 s to either a single pattern or a pair of patterns with particular regularities, and the perceived regularities of subsequently presented test patterns were measured using a conventional staircase matching procedure. We found that the regularity aftereffect (RAE) was unidirectional: Adaptation only caused test patterns to appear less regular. We also found that RAEs transferred from GB adaptors to both DOG and RBP test patterns and from DOG and RBP adaptors to GB patterns. We suggest that regularity is coded by the peakedness in the distribution of spatial-frequency channel responses across scale, and that the RAE is a result of a flattening of this distribution by adaptation. Thus, the RAE may be a consequence of contrast normalization, and an example of norm-based coding where irregularity is the norm.