Abstract
Order statistic filters are used often in the applications of science and engineering problems. This paper investigates the design and training of a feed-forward neural network to approximate minimum, median and maximum operations. The design of order statistic neural network filtering (OSNNF) is further refined by converting the input vectors with elements of real numbers to a set of inputs consisting of ones and zeros, and the neural network is trained to yield a rank vector which can be used to obtain the exact ranked values of the input vector. As a case study, the OSNNF is used to improve the visibility of target echoes masked by clutter in ultrasonic nondestructive testing applications.
Highlights
Order statistic (OS) processors have been widely used in the field of signal and image processing [1,2,3]
This paper investigates the design and training of a feed-forward neural network to approximate minimum, median and maximum operations
The design of order statistic neural network filtering (OSNNF) is further refined by converting the input vectors with elements of real numbers to a set of inputs consisting of ones and zeros, and the neural network is trained to yield a rank vector which can be used to obtain the exact ranked values of the input vector
Summary
Order statistic (OS) processors have been widely used in the field of signal and image processing [1,2,3]. OS results can be obtained by sorting the elements of an input vector according to the rank of each element Ranked outputs such as minimum, median and maximum have been used for target detection with applications in radar, sonar and ultrasonic nondestructive testing [4,5]. In this paper, feed-forward neural network models [12] are introduced to find the minimum, the median, and the maximum of the input vectors consisting of real numbers. The trained OSNNF filter might not provide exact sorted results. The neural network is trained to yield the ranked output (e.g., the minimum, the median or the maximum value of the input vector with real numbers).
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