Abstract
An algorithm based on pulse-coupled neural network (PCNN) constructed in the Tetrolet transform domain is proposed for the fusion of the visible and passive millimeter wave images in order to effectively identify concealed targets. The Tetrolet transform is applied to build the framework of the multiscale decomposition due to its high sparse degree. Meanwhile, a Laplacian pyramid is used to decompose the low-pass band of the Tetrolet transform for improving the approximation performance. In addition, the maximum criterion based on regional average gradient is applied to fuse the top layers along with selecting the maximum absolute values of the other layers. Furthermore, an improved PCNN model is employed to enhance the contour feature of the hidden targets and obtain the fusion results of the high-pass band based on the firing time. Finally, the inverse transform of Tetrolet is exploited to obtain the fused results. Some objective evaluation indexes, such as information entropy, mutual information, and QAB/F, are adopted for evaluating the quality of the fused images. The experimental results show that the proposed algorithm is superior to other image fusion algorithms.
Highlights
Both the active mode and passive mode are used to detect concealed objects
The statistics-based metrics are influenced by the pseudoedges of targets, so we evaluate the fusion performance based on information-based and human-visual-system based metrics
An improved pulse-coupled neural network (PCNN) for the fusion of the passive millimeter wave (PMMW) and visible image is proposed in the Tetrolet domain
Summary
The active detection mode usually relies on strong penetrability of special ray. It (i.e., the irreversible radiation) is easy to damage the testing material and human health. The passive detection mode plays an important role in the field of threat precaution due to its security. It depends on the spectral radiation difference between the interesting targets and surrounding for recognizing the concealed objects. Some completely different pixels are generated for describing the information of brightness temperature in PMMW images, which lead to an obvious diversity between the gun and human body. The target characteristic forming in the PMMW images is different from surroundings, which leads to an automated target detection [2]
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