Image decomposition using directional filter banks is useful in discovering and extracting edge orientation cues for
target detection in airborne surveillance images. Since images of interest are very large and the filtered images are not
downsampled in the application of interest, conventional filtering can be computationally extremely demanding and
there is a need to explore procedures to make the filtering efficient. In this paper a novel filter bank structure for
directional filtering of images is proposed and its design described. The design is carried out by imposing structural
constraints on the filters, which are implemented using a generalized notion of separable filtering. The structure uses
one-dimensional (1-D) filters as building blocks, which are employed in novel configurations to obtain filters with
narrow wedge-shaped passbands. Design procedures have been developed for constructing 16-band, 32-band, and 64-
band partitions starting with either built-in or user-specified 1-D prototypes. Implementations of filters using the
proposed method show significant improvement compared with conventional implementation, often more by an order of
magnitude, which is also supported by a theoretical analysis of the filter complexity.
Efficient processing of imagery derived from remote sensing systems has become ever more important due to increasing
data sizes, rates, and bit depths. This paper proposes a target detection method that uses a special class of wavelets based on
highly frequency-selective directional filter banks. The approach helps isolate object features in different directional filter
output components. These components lend themselves well to the application of powerful denoising and edge detection
procedures in the wavelet domain. Edge information is derived from directional wavelet decompositions to detect targets
of known dimension in electro optical imagery. Results of successful detection of objects using the proposed method are
presented in the paper. The approach highlights many of the benefits of working with directional wavelet analysis for
image denoising and detection.
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