We simulate and study various computational techniques for image reconstruction applied to a multiplexed Mid-wave-IR Imager. The imager consists of two arms where one is dedicated to high resolution imaging using a lower resolution Focal Plane Array and the other is a single measurement Multispectral imager which uses dispersive optics. We exploit the compressibility of images to estimate a high resolution image and its corresponding lower resolution multispectral data cube using standard computational techniques.
In this paper we describe the algorithm for local image reconstructions from global measurements on the Focal Plane Array (FPA). The global measurements may come from a multiplexed imaging and /or convolution-based sampling model. The algorithm consists of scanning a rectangular segment on the FPA data and reconstructing the image on that segment using a modified Wiener Filter by adapting the measurements on the data via a linear operator. This method is essential in the reconstruction of large format images from large data samples. In particular, in this paper the method is applied to multiplexed, multispectral imaging from a single measurement on the FPA.
We look at the design of projective measurements based upon image priors. If one assumes that image patches
from natural imagery can be modeled as a low rank manifold, we develop an optimality criterion for a measurement matrix based upon separating the canonical elements of the manifold prior. Any sparse image reconstruction
algorithm has improved performance using the developed measurement matrix over using random projections.
We implement a 2-way clustering then K-means algorithm to separate the estimated image space into low dimensional clusters for image reconstruction via a minimum mean square error estimator. Some insights into the
empirical estimation of the image patch manifold are developed and several results are presented.