  In this paper, we introduce a machine learning approach to the problem of
infrared small target detection filter design. For this purpose, similarly to a
convolutional layer of a neural network, the normalized-cross-correlational
(NCC) layer, which we utilize for designing a target detection/recognition
filter bank, is proposed. By employing the NCC layer in a neural network
structure, we introduce a framework, in which supervised training is used to
calculate the optimal filter shape and the optimum number of filters required
for a specific target detection/recognition task on infrared images. We also
propose the mean-absolute-deviation NCC (MAD-NCC) layer, an efficient
implementation of the proposed NCC layer, designed especially for FPGA systems,
in which square root operations are avoided for real-time computation. As a
case study we work on dim-target detection on mid-wave infrared imagery and
obtain the filters that can discriminate a dim target from various types of
background clutter, specific to our operational concept.
