Compressed Sensing mm-Wave SAR for Non-Destructive Testing Applications Using Multiple Weighted Side Information

Abstract

This work explores an innovative strategy for increasing the efficiency of compressed sensing applied on mm-wave SAR sensing using multiple weighted side information. The approach is tested on synthetic and on real non-destructive testing measurements performed on a 3D-printed object with defects while taking advantage of multiple previous SAR images of the object with different degrees of similarity. The tested algorithm attributes autonomously weights to the side information at two levels: (1) between the components inside the side information and (2) between the different side information. The reconstruction is thereby almost immune to poor quality side information while exploiting the relevant components hidden inside the added side information. The presented results prove that, in contrast to common compressed sensing, good SAR image reconstruction is achieved at subsampling rates far below the Nyquist rate. Moreover, the algorithm is shown to be much more robust for low quality side information compared to coherent background subtraction.

Designing Recurrent Neural Networks by Unfolding an L1-L1 Minimization Algorithm

“…After 200 epochs of training our model, we measured the sparsity (number of zero elements) of 55% in the last layer h (3) t . We leave the investigation of how sparsity affects reconstruction performance for future work.…”

“…The problem of reconstructing sequential signals from lowdimensional-and possibly corrupted-observations across time appears in various imaging applications, including compressive video sensing [1], dynamic magnetic resonance imaging [2], and mm-Wave imaging [3]. When reconstructing time-varying signals, one needs to leverage prior knowledge; namely that (i) at a given time instance the signal has a low-complexity representation, such as sparsity in a learned dictionary or fixed basis, and (ii) signals (or their representations) across time are correlated (temporal correlation).…”

GWO-Based Joint Optimization of Millimeter-Wave System and Multilayer Perceptron for Archaeological Application

“…Due to the size of the pores, alignments of the order of the millimeter, CT-scan [36], or synchrotron [37] were previously used but they are bulky and expansive. On the contrary, low-THz frequency radar-based techniques [38,39] provide compact systems while offering high lateral resolution within the diffraction limit of half a wavelength (typ. 1.5 mm at 100 GHz) [35,38,39].…”

“…On the contrary, low-THz frequency radar-based techniques [38,39] provide compact systems while offering high lateral resolution within the diffraction limit of half a wavelength (typ. 1.5 mm at 100 GHz) [35,38,39].…”

Survey on compressed sensing reconstruction method for 3D data

“…As an efficient signal processing method, compressed sensing (CS) is active in various neighborhoods, such as wireless sensor network, 1 magnetic resonance imaging, 2 electron tomography, 3 synthetic aperture radar image sensing 4 and so forth. However, the representation of data presents diverse characteristics in rapidly change of technology 5 .…”