Abstract:
Two-dimensional near-field millimeter-wave (MMW) imaging systems face challenges in achieving high-resolution (HR) images due to constraints in device size and sampling t...Show MoreMetadata
Abstract:
Two-dimensional 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 article 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 an SR algorithm. The algorithm employs wavelet transform domain norm and TV operators to create a mixed sparse function for multifrequency 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.
Published in: IEEE Transactions on Instrumentation and Measurement ( Volume: 74)