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Anisotropic and directional filters can smooth noisy images while preserving object boundaries. Data from remote sensing instruments often have missing pixels due to geometric or power limitations. In such cases, these nonisotropic filters are very inefficient, because transform methods cannot be used when there is missing data or when logical operations need to be performed. A directional filter is introduced in this letter that retains the ability to handle missing data and is separable, making it computationally efficient. We demonstrate the directional filter on weather radar data where it can be used to smooth along fronts. Since the filter introduced here can be parameterized for scale, orientation, and aspect ratio, this filter can be used in any directional filtering application where transform methods cannot be used, but computational efficiency is desired.