Multi-sensor industrial vision through scattering media via fusing experiment design and regularization theory methods

In this paper, the authors address a new approach to industrial vision and remote sensing using multi-sensor imaging systems stated and treated as ill-posed inverse problems of restoring the power (brightness) distribution of the extended object reflected signals distorted in a stochastic scattering medium. The developed approach is based on the constrained regularization technique for inversion of the signal formation operator of the stochastic measurement channel. To transfer the generic ill-posed inverse imaging problem to its system-oriented numerical version, the experiment design methodology is applied. Experiment design theory-based fusion of diverse information on data sets and image space in a generalized constrained imaging problem model is the first issue addressed in the paper. Solution of this problem in the mixed experiment design and regularization setting that results in developing the regularized numerical technique for extended object imaging through the scattering random medium with improved spatial resolution is the second issue this paper addresses.