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A number of methods have been developed in the past for color image enhancement, including retinex and color
constancy algorithms. Retinex theory is based on psychophysical experiments using mondrian patterns.
Recently, multi-scale retinex algorithms have been developed. They combine several "Retinex" outputs to produce a
single output image which has both good dynamic range compression and color constancy, as well as good tonal
rendition. Unfortunately, multi-scale retinex processing time is consuming. In this paper we present a new algorithm
for color and thermal image enhancement. Additionally, an experimental prototype system for fusing the two data
types with depth data to create a three-dimensional map of the datasets is presented. The image processing algorithm
utilizes a combination of fourier domain and retinex algorithms. Different types of thermal and natural
scene NASA images have been tested, along with other imagery. The primary advantages of the image processing
algorithm are the reduced computational complexity and the contrast enhancement performance. Experimental
results demonstrate that the algorithm works well with underexposed images. The algorithm also gives better
contrast enhancement in most cases, thus bringing out the true colors in the image. It thus helps in achieving
both color constancy and local contrast enhancement. We compare the presented method with enhancement based
on NASA's Multi-scale Retinex. Statistically and quantitatively, we have shown that our technique indeed results in
enhanced images, with our argument validated by conducting experiments on human observers. Additionally, the
fusion of 2-dimensional (2D) thermal, 2D RGB, and 3-dimensional (3D) depth data (TRGBD) can be analyzed and
researched for the purpose of extrapolating thermal conductance and other thermal properties within a scanned
environment. This will allow for the determination of energy assessments regarding structural boundaries, the
effectiveness of insulation, leakages of heat, water and refrigerant, and computing the true value of observed thermal
losses/gains as they are related not only to thermal properties, but geometries as well. The data generated from this
analysis can be used in many other domains and process evaluation fields such as medical, geological, architectural
and others.
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