Ultrawideband (UWB) SAR images often contain clutter and other types of spatially correlated noise. To reduce these effects, we have developed a complex, multichannel, autoregressive image- modeling and inverse-filtering algorithm. Our algorithm extends a previously reported technique to include complex image data and a solution to the directionality problem of 2D AR models. A multichannel image pixel, represented by a vector, is modeled as an autoregressive process with a nonsymmetric half-plane (NSHP) support region. The parameters of this NSHP AR model, represented as matrices, are estimated by minimizing a squared residual. To account for possible nonstationarity in the image statistics, the estimation is performed over a series of small subwindows, which lends a degree of adaptabtility to the algorithm. Once estimates of the complex AR parameter matrices are obtained, they are used in an inverse filter. To account for the directionality inherent in the NSHP filter, we have adopted a bi-directional filtering approach, with the results of the two filters combined such that targets are enhanced and clutter is minimized. This algorithm has been applied to various UWB SAR images and shown to be an effective whitening filter. The resulting signal-to-clutter enhancement depends on a variety of factors, including clutter statistics, target size, and AR model order. A version of the whitening algorithm has been incorporated into our standard set of tools for processing UWB SAR images.