Wave-informed dictionary learning for high-resolution imaging in complex media
This work addresses imaging challenges in complex media for applications like geophysics or medical imaging, but it appears incremental as it builds on existing dictionary learning and time reversal methods.
The paper tackles high-resolution imaging in scattering media by proposing a two-step dictionary learning approach that estimates Green's function vectors and orders them for imaging, achieving resolution comparable to a homogeneous medium in simulations.
We propose an approach for imaging in scattering media when large and diverse data sets are available. It has two steps. Using a dictionary learning algorithm the first step estimates the true Green's function vectors as columns in an unordered sensing matrix. The array data comes from many sparse sets of sources whose location and strength are not known to us. In the second step, the columns of the estimated sensing matrix are ordered for imaging using Multi-Dimensional Scaling with connectivity information derived from cross-correlations of its columns, as in time reversal. For these two steps to work together we need data from large arrays of receivers so the columns of the sensing matrix are incoherent for the first step, as well as from sub-arrays so that they are coherent enough to obtain the connectivity needed in the second step. Through simulation experiments, we show that the proposed approach is able to provide images in complex media whose resolution is that of a homogeneous medium.