A Compressive Super-Resolution Imaging Algorithm for Multifrequency Near-Field Millimeter-Wave

Two-dimensional (2-D) near-field millimeter-wave (MMW) imaging systems face challenges in achieving high-resolution (HR) images due to constraints in device size and sampling time. These limitations affect the quality of target images and subsequent scientific measurements. This paper addresses this problem by proposing a novel super-resolution (SR) algorithm for near-field MMW imaging data processing. The proposed method introduces a compressive sensing-based optimization factor and develops a SR algorithm. The algorithm employs wavelet transform domain norm and TV operators to create a mixed sparse function for multi-frequency scanning data to facilitate HR image reconstruction. Experimental results demonstrate that the proposed method outperforms existing SR techniques in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index, producing images with superior objective quality evaluation and visual quality. This approach offers a cost-effective solution to enhance MMW imaging performance without requiring expensive hardware upgrades.