Quality improving of millimeter wave images using fusion with visible images

Passive millimeter wave imaging is using to discover hidden objects under human clothes. Discovering hidden objects in the places such as airports, due to their security, is extremely important. However, millimeter wave images have low-quality and image processing techniques are needed to improve the quality of the images. This paper attempts to present a method of fusion approach to discover hidden objects from PMMW images and preserve the detail of visible images. In the proposed method, images are subdivided using BEMD conversion into high frequency and low frequency sub-images. In the next step, the NSST conversion is used to parse images from the previous step in different resolutions and directions, and then the improved SCM neural network is used as the fusion rule. The results are evaluated using fusion effectiveness criteria of QAB/F and MI. Simulation results show that the proposed method improves the best previous results, which were combined using NSST analysis method and ISCM law, with an average of about 33% for the QAB/F criterion.