Image Processing Reference

In-Depth Information

The first technique is based on an edge-preserving filter known as the bilateral filter

for the calculation of the

α

-matte. The hyperspectral bands contain a large number of

objects and features. Some of the features are weak, and visually apparent over only

a very small number of bands. The use of a bilateral filter enables us to extract these

weak and minor features from the hyperspectral data which might get lost during the

process of fusion otherwise. The fusion weights are calculated from these extracted

features in order to accentuate their representation in the fused image. This technique

uses the edge-preserving property of the bilateral filter in order to define the fusion

weights which are functions of the locally dominant features within the bands. This

technique exploits all available information at all locations in all bands, unlike the

methods that assign the same weight for the entire band. This solution turns out to

be very fast with the availability of faster implementations of the bilateral filter.

We have also explained a hierarchical scheme for an efficient implementation of

the bilateral filtering-based solution. This scheme can accommodate any increase in

the number of bands with a minimal degradation in the quality of the fused image.

This implementation operates over only a fraction of data at any given instant, and

therefore, does not require the entire data to be loaded into the memory. This scheme

allows fusion of bands up to any given spectral bandwidth for the midband visualiza-

tion which can be useful if the features from a certain set of bands need highlighting.

The hierarchical scheme is generic, and therefore can be suitably applied over any

pixel-based fusion techniques.

We have then developed a Bayesian framework for fusion of hyperspectral data.

Here, fusion has been posed as an estimation problem where the fused image has

been regarded as the
true scene
to be estimated. This fusion solution employs a

model of image formation which relates the fused image with the input hyperspectral

data through a first order approximation. We discussed an approach to determine the

parameters of this model which indicate the closeness of the input data from the fused

image to be estimated. When the fused image is meant for visualization, one expects

a higher contribution towards fusion from the pixels that possess a high amount

of visually important information. This fusion technique, thus, utilizes some of the

characteristics of the input hyperspectral bands to determine the parameters of this

model. It considers the well-exposedness and the sharpness of pixels as the quality

measures for the computation of these model parameters. Like the first solution,

this solution also operates on a per pixel basis in order to exploit the entire data.

The fused image is then estimated using the MAP framework. The prior term in the

MAP formulation has been a total variation (TV) norm-based penalty function to

acknowledge the intra-pixel spatial correlation in natural images. The advantage of

the TV norm-based penalty lies in its discontinuity preserving characteristic which is

responsible for producing the resultant image with a high degree of sharpness. Apart

from the output quality, the Bayesian fusion technique provides other advantages

like flexibility in the computation of the corresponding model parameters through

the choice of various image quality measures, and the availability of a large number

of tools from the estimation theory.

The variational technique developed for fusion of hyperspectral images elimi-

nates the process of generating fusion mattes. The fusion weights which act as the