**2.2.1 The nonlinear log transform**

The non-linear log transform converts an original image *g* into an adjusted image *g′* by applying the log function to each pixel *g*[*m, n*] in the image,

$$\log'[m,n] = k \log(\mathcal{g}[m,n])\tag{1}$$

where *k*=*L/log*(*L*) is a scaling factor that preserve the dynamic range and *L* is intensity. The log transformis typically applied either to dark images where the overall contrast is low, or to images that contain specular reflections or glints. In the former case, the brightening of the dark pixels leads to an overall increase in brightness. In the latter case, the glints are suppressed thus increasing the effective dynamic range of the image.

The log function as defined in equation 1 is not parameterized, i.e. it is a single input/output transfer function. A modified parameterized function was proposed by Schreiber in [W. F. Schreiber, 1978] as: image,

$$\log'(l) = (L - 1) \left[ \frac{\log(1 + \alpha \lg(l)) - \log(\alpha + 1)}{\log(1 + \alpha L) - \log(\alpha + 1)} \right] + 1 \tag{2}$$

where αparameterizes the non-linear transfer function.

### **2.3 Registration**

188 Bio-Inspired Computational Algorithms and Their Applications

Section 4 presents the proposed image fusion algorithm. Section 5 reports the experimental

In this section, we will present related work in IR Image technology, nonlinear image

One type of electromagnetic radiation that has received a lot of attention recently is Infrared (IR) radiation. IR refers to the region beyond the red end of the visible color spectrum, a region located between the visible and the microwave regions of the electromagnetic spectrum.

Today, infrared technology has many exciting and useful applications. In the field of infrared astronomy, new and fascinating discoveries are being made about the Universe and

Humans, at normal body temperature, radiate most strongly in the infrared, at a wavelength of about 10 microns. The area of the skin that is directly above a blood vessel is, on average, 0.1 degrees Celsius warmer than the adjacent skin. Moreover, the temperature variation for

In fact, variations among images from the same face due to changes in illumination, viewing direction, facial expressions, and pose are typically larger than variations introduced when different faces are considered. Thermal IR imagery is invariant to variations introduced by illumination facial expressions since it captures the anatomical information. However, thermal imaging has limitations in identifying a person wearing glasses because glass is a material of low emissivity, or when the thermal characteristics of a face have changed due to increased body temperature (e.g., physical exercise) [G. S. Kong et al., 2005]. Combining the

The non-linear log transform converts an original image *g* into an adjusted image *g′* by

where *k*=*L/log*(*L*) is a scaling factor that preserve the dynamic range and *L* is intensity. The log transformis typically applied either to dark images where the overall contrast is low, or to images that contain specular reflections or glints. In the former case, the brightening of the dark pixels leads to an overall increase in brightness. In the latter case, the glints are

The log function as defined in equation 1 is not parameterized, i.e. it is a single input/output transfer function. A modified parameterized function was proposed by Schreiber in [W. F.

*g′*[*m, n*] = *k*log(*g*[*m, n*]) (1)

a typical human face is in the range of about 8 degrees Celsius [F. Prokoski, 2000].

IR and visual techniques will benefit face detection and recognition.

suppressed thus increasing the effective dynamic range of the image.

**2.2 Nonlinear image enhancement techniques** 

applying the log function to each pixel *g*[*m, n*] in the image,

**2.2.1 The nonlinear log transform** 

Schreiber, 1978] as: image,

results of the proposed algorithm. Section 6 concludes this research.

enhancement algorithms, image registration and image fusion.

**2. Literature survey** 

medical imaging as a diagnostic tool.

**2.1 IR tecnology** 

Image registration is a basic task in image processing to align two or more images, usually refereed as a reference, and a sensed image [R. C. Gonzalez et al., 2004]. Registration is typically a required process in remote sensing [L. M. G. Fonseca & B. S. Manjunath, 1996], medicine and computer vision. Registration can be classified into four main categories according to the manner how the image is obtained [B. Zitova & J. Flusser, 2003]:


It is impossible to implement a comprehensive method useable to all registration tasks and there are many different registration algorithms. The focus is on the feature based registration techniques in this research and they usually consist of the following three steps [B. Zitova & J. Flusser, 2003].


Each registration step has its specific problems. In the first step, features that can be used for registration must spread over the images and be easily detectable. The determined feature sets in the reference and sensed images must have enough common elements, even though the both images do not cover exactly the same scene. Ideally, the algorithm should be able to detect the same features [B. Zitova & J. Flusser, 2003].

In the second step, known as feature matching, physically corresponded features can be dissimilar because of the different imaging conditions and/or the different spectral sensitivities of the sensors. The choice of the feature description and measuring of similarity has to take into account of these factors. The feature descriptors should be efficient and invariant to the assumed degradations. The matching algorithm should be robust and efficient. Single features without corresponding counterparts in the other image should not affect its performance [B. Zitova & J. Flusser, 2003].

In the last step, the selection of an appropriate resampling technique is restricted by the trade-off between the interpolation accuracy and the computational complexity. In the

Fusion of Visual and Thermal Images Using Genetic Algorithms 191

Define cost function, variables

Generate initial population

Find cost for each chromosome

Select mates

Mating

Mutation

Converge Check

done

In this case, the variable values are represented as floating-point numbers. Each chromosome has a cost found by evaluating the cost function *f* at the variables

Equations (3) and (4) along with applicable constraints constitute the problem to be solved. Since the GA is a search technique, it must be limited to exploring a reasonable region of variable space. Sometimes this is done by imposing a constraint on the problem. If one does not know the initial search region, there must be enough diversity in the initial population to explore a reasonably sized variable space before focusing on the most promising regions.

To begin the CGA process, an initial population of *Npop* must be defined, a matrix represents the population, with each row being a 1x *N*var chromosome of continuous values [D.Patnaik, 2006]. Given an initial population of *N pop* chromosomes, the full matrix of *N x pop*

All variables are normalized to have values between 0 and 1. If the range of values is

cost = *f* (chromosome) = *f* ( <sup>123</sup> var , , ,...., *<sup>N</sup> p pp p* ) (4)

var (,) *pop pop rand N N* = (5)

( ) *hi lo norm lo p p pp p* =− + (6)

Fig. 1. Flowchart of CGA

**2.4.2.1.2 Initial population** 

where

*N*var random values is generated by:

between *lo p* and *hi p* , then the normalized values are given by:

<sup>123</sup> var , , ,...., , *<sup>N</sup> ppp p* 

literature, there are popular techniques such as the nearest-neighbor and bilinear interpolation [B. Zitova & J. Flusser, 2003].

### **2.4 Genetic Algorithm**
