Abstract
Compressive 2D near-field millimetre-wave (MMW) imaging is a compressed sensing-based technique, which reconstructs an image from a few under-sampled measurements. Although this technique can extremely improve imaging process efficiency, resolution of the reconstructed image is still limited by the scanning grid interval, which is always hard to be improved in practice due to hardware constraint. To reconstruct a higher resolution image from under-sampled measurements, a compressive 2D near-field MMW super-resolution (SR) imaging model is established. Meanwhile, the corresponding algorithm, which is based on the primal-dual framework, is also proposed. Experimental results show that the proposed algorithm can effectively reconstruct the SR MMW image in superior performance.
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