Two-dimensional (2-D) adaptive filtering is a technique that can be applied to many image processing applications. This paper will focus on the development of an improved 2-D adaptive lattice algorithm (2-D AL) and its application to the removal of correlated clutter to enhance the detectability of small objects in images. The two improvements proposed here are increased flexibility in the calculation of the reflection coefficients and a 2-D method to update the correlations used in the 2-D AL algorithm. The 2-D AL algorithm is shown to predict correlated clutter in image data and the resulting filter is compared with an ideal Wiener-Hopf filter. The results of the clutter removal will be compared to previously published ones for a 2-D least mean square (LMS) algorithm. 2-D AL is better able to predict spatially varying clutter than the 2-D LMS algorithm, since it converges faster to new image properties. Examples of these improvements are shown for a spatially varying 2-D sinusoid in white noise and simulated clouds. The 2-D LMS and 2-D AL algorithms are also shown to enhance a mammogram image for the detection of small microcalcifications and stellate lesions.

The paper focuses on the development of an improved 2D adaptive lattice algorithm (2D-AL) and its application to the removal of correlated clutter to enhance the detectability of small objects in images. The two improvements proposed are increased flexibility in the calculation of the reflection coefficients and a 2D method to update the correlations used in the 2D-AL algorithm. The results of the clutter removal is compared to previously published ones for a 2D LMS (TDLMS) algorithm. 2D-AL is better able to predict spatially varying clutter than the TDLMS algorithm since it converges faster to new image properties. Examples of these improvements are shown. The TDLMS and 2D-AL algorithms are also shown to enhance a mammogram images for the detection of small microcalcifications.>