An Optimum Method for Concealed Object Detection based on Passive Millimeter Wave Imaging

Passive Millimeter Wave (PMMW) imaging makes it possible to acquire images of objects that are concealed underneath clothing by measuring the radiometric temperatures of different objects on a human subject, that provides a great advantage for security systems in special places like airports. Due to the inherent physical properties of PMMW imaging and the associated hardware, images have poor contrast and low signal to noise ratio. The fundamental operation underlying the sensing operation is the low-pass filtering effect due to the finite size of the antenna lens. The image at the output of the imaging system is a low-pass filtered and blurred version of the original scene. In order to improve the resolution and the fusion quality of the image, some methods of image restoration, super-resolution and fusion will be needed. In the process of restoration, the point spread function is used for the inverse restoration of the original image. Image fusion is the technique of quality improvement that intelligently integrates and processes the multi-source images from various sensors and obtains more accurate, complete and dependable descriptions about the same scene compared with than any of the individual source images. In this paper, various methods for image restoration, super-resolution and fusion are reviewed. Then, appropriate methods in both contexts are selected and modified for the special considered usage. Also, an appropriate generic procedure is designed for PMMW quality improvement in concealed object detection applications. By implementing this procedure, noticeable visual improvement is found in the output image quality.