A High Resolution Imaging Algorithm for Synthetic Aperture Interferometric Radiometers in Near-Field

Synthetic aperture interferometric radiometers (SAIR) has been introduced to threat detection for high spatial resolution and no harm to human body. Usually, the SAIR security instrument is about 3 meters away from human body, which means that the SAIR works at near-field and the relationship between the visibility and the brightness temperature is no longer Fourier Transform. The contours and details of prohibited items are blurry in rebuild image by the traditional inversion method, such as Moore-Penrose method and Tikhonov regularization, so it is difficult to identify prohibited items. In this study, a regularization model based on gradient L1 norm minimization is proposed. In image processing, the contour can be regarded as a marker line where the brightness temperature changes sharply along the vertical direction. So, the gradient filed of the brightness temperature map is appropriate to quantify the contours. And the L1 norm minimization model is able to guarantee the inversion accuracy and enhance the contour. Simulation for SAIR is performed to validate the contour enhancement imaging method. A complex scene consisting of many small regions with different shape and brightness temperature value corresponding to different prohibited items is created. The reconstructed image by proposed method is compared with the results by Moore-Penrose method and Tikhonov regularization. The proposed method shows better reconstructed image quality.