An improved 2-D adaptive lattice filtering algorithm and its application to detection of small objects in correlated clutter

Ffrench, P.A.; Zeidler, J.R.; Ku, W.H.

doi:10.1109/icip.1994.413418

Cited by 2 publications

(4 citation statements)

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“…The effect is that the resulting number of different reflection coefficients is reduced by a factor of 2 from 12 to 6, which was shown by Watanabe et al [7]. The cross-correlation values that are the same for a stationary image are 28If these values are placed into (17), (20), and (21), the resulting equal reflection coefficients for the stationary case are (29)…”

Section: A Development Of a Single 2-d Al Lattice Stagementioning

confidence: 87%

“…, and . It can be rewritten as (16) The reflection coefficients are thus calculated by (17) and contain autocorrelations and crosscorrelations values of the inputs of each stage. They are described by the following equations: It is very desirable to update the correlation values recursively; i.e., the correlation at each pixel is based on some previous values at pixels and .…”

Section: A Development Of a Single 2-d Al Lattice Stagementioning

confidence: 99%

“…The calculation of the reflection coefficients produce the exact weighted least-squares solution for a single lattice stage, since (9) is solved for exactly. The inverse is calculated directly in (17), which is in contrast to the iterative method used to solve for the inverse in the weighted recursive least squares (WRLS) algorithm [18]. The inverse can be quickly calculated, since the size of the matrix is small.…”

Section: A Development Of a Single 2-d Al Lattice Stagementioning

confidence: 99%

“…Finally, some results for the processing of mammogram data by 2-D AL and 2-D LMS are presented. More detailed discussions of the ideas presented in this paper can be found in [17].…”

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confidence: 99%

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Enhanced detectability of small objects in correlated clutter using an improved 2-D adaptive lattice algorithm

Ffrench¹,

Zeidler

1997

IEEE Trans. on Image Process.

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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.

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Section: A Development Of a Single 2-d Al Lattice Stagementioning

confidence: 87%

Section: A Development Of a Single 2-d Al Lattice Stagementioning

confidence: 99%

Section: A Development Of a Single 2-d Al Lattice Stagementioning

confidence: 99%

“…Finally, some results for the processing of mammogram data by 2-D AL and 2-D LMS are presented. More detailed discussions of the ideas presented in this paper can be found in [17].…”

mentioning

confidence: 99%

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