Interferometric inverse synthetic aperture radar (InISAR) imaging for sparse-aperture (SA) data is still a challenge, because the similarity and matched degree between ISAR images from different channels are destroyed by the SA data. To deal with this problem, this paper proposes a novel SA-InISAR imaging method, which jointly reconstructs 2-dimensional (2-D) ISAR images from different channels...

Abstract An important element of 3D data-driven simulations solar magnetic fields is the determination horizontal electric field at photosphere. This used to drive and inject energy helicity into corona. One outstanding problem localisation such that it consistent with Ohm’s law. Yeates ( Astrophys. J. 836 (1), 131, 2017) put forward a new “sparse” technique for computing from normal-component ...

Basis pursuit inversion of seismic reflection data for reflection coefficients is introduced as an alternative method of incorporating a priori information in a seismic inversion process. The inversion is accomplished by building a dictionary of functions, representing seismic reflection responses, and constituting the seismic trace as a superposition of these responses. Basis pursuit decomposi...

An efficient nonlinear contrast source inversion scheme for electromagnetic imaging of sparse two-dimensional investigation domains is proposed. To avoid generating a sequence linear optimization problems, the non-linearity directly tackled using Landweber (NLW) iterations. A self-adaptive projected accelerated steepest descent (A-PASD) algorithm incorporated to enhance efficiency NLW The enfor...

X-ray Computed Tomography (CT) is widely used in clinical applications such as diagnosis and image-guided interventions. In this paper, we propose a new deep learning based model for CT image reconstruction with the backbone network architecture built by unrolling an iterative algorithm. However, unlike existing strategy to include many data-adaptive components unrolled dynamics possible, find ...