| dc.description.abstract | In this work we address the problem of acquisition of multispectral
images in a sampled form and the subsequent processing of the
acquired signal. The problem is relevant in the context of color
imaging in digital cameras, and increasingly, in the field of
hyperspectral imaging as applied to remote-sensing and target
recognition. The scope of this work encompasses a broad swath
across image processing problems and includes: image acquisition, in
the problem of optimally selecting sampling rates and patterns of
multiple channels; image reconstruction, in the reconstruction of
the sparsely sampled data; image restoration, in obtaining an
estimate of the true scene from noisy data; and finally, image
enhancement and representation, in the problem of presenting the
reconstructed image in a color-space that allows for transformations
that achieve best perceived quality.
Acquisition of multispectral images in the simplest form entails
either the use of multiple sensor arrays to sample separate spectral
bands in a scene, or the use of a single sensor array with a
mechanism that switches overlaying band-pass filters. Due to the
nature of the acquisition process, both these methods suffer from
shortcomings in terms of weight, cost, time of acquisition, etc. An
alternative scheme widely in use only uses one sensor array to
sample multiple bands. An array of filters, referred to as a
\textit{mosaic}, is overlaid on the sensor array such that only one
color is sampled at a given pixel location. The full color image is
obtained during a subsequent reconstruction step commonly referred
to as \textit{demosaicking}. This scheme offers advantages in terms
of cost, weight, mechanical robustness and the elimination of the
related post-processing step since registration in this case is
exact.
Three main issues need to be addressed in such a scheme, viz., the
shape and arrangement of the sampling pattern, selection of the
sensitivities of the spectral filters, and the design of the
reconstruction algorithm. Each of the above problems is contingent
on multiple factors. Sensor sampling patterns are constrained by the
limitations of electronic devices and manufacturing processes,
spectral sensitivities are affected by the material properties of
the colors painted on the array to form filters, and the
reconstruction methods are limited by computational resources.
In this research, we address the above problems from a signal
processing perspective and attempt to develop parametric algorithms
that can accommodate external limitations and constraints. We have
developed methodologies for the selection of optimal sampling
patterns that will allow for ordered, repeated array blocks. In
addition we have developed an algorithm for demosaicking of CFA data
based on Bayesian techniques. We have also proposed a formulation
for the selection of optimal spectral sensitivities for individual
color filters. | en_US |
