Passive Millimeter Wave Image Fusion Algorithm Based On Pocs And Fuzzy Processing

Passive millimeter-wave imaging has become a research hotspot in the area of millimeter-wave since the 1990s. At present, the imaging systems suffer the drawbacks of low spatial resolution and poor sensitivity. With the wide application of passive millimeter-wave imaging, it is vital to improve the quality of the passive millimeter-wave images. Hence, an algorithm is needed to restore the degraded images under the condition of the hardware unchanged.Based on the essential theory of passive millimeter wave imaging, the passive millimeter wave imaging system is introduced in this paper, and then both the imaging model and the causes of degradation to passive millimeter wave are also demonstrated here. Owing to the fact that there are many reasons related with the degradation of passive images, the super-resolution image restoration algorithm for passive millimeter-wave images is introduced.After the analysis to the limitation of super-resolution in frequency domain and the superiority of that in spatial domain, two super-resolution algorithms in the spatial domain—maximum a posteriori probability (MAP) and projection onto convex test (POCS). On the basis of the comparison of these two kinds of algorithm, the POCS is adopted in this paper. Moreover, the fuzzy theory is introduced here to improve the POCS algorithm.Finally, the algorithm is implemented in the platform of MATLAB, and a series of experiments conducted to both simulation images and realistic passive millimeter-wave images are carried out. The experiment results demonstrate the significance of the algorithm.