An alternative to least-squares imaging using data-domain matching filters

Posing migration as an inverse or a least-squares problem can improve the quality of imaging. This class of techniques can resolve illumination issues and improve focusing. Standard iterative least-squares imaging can be expensive and results are often compromised. We present a procedure using matching filters operating in data-space rather than image space. Effective inversion results are demonstrated on synthetic and real data.