**3.4 Conclusion**

200 Bio-Inspired Computational Algorithms and Their Applications

<sup>≤</sup> <sup>=</sup>

σ

3. Let *Sn* be normalized sharpness parameter, such that *Sn* =*min*(2.0,*S*/100). When *Sn* >0.8,

 0.5 0.1 *Q S* = ++ μ

*nn n* σ

where 0 1.0 < < *Q* is the quality factor. A region is classified as good when *Q* > 0.55,

The image samples for ETNUD were selected to be as diverse as possible so that the result would be as general as possible. MATLAB was used for AINDANE and IRME algorithms and their codes were developed by the author and research team. MSRCR enhancement was done with commercial software, Photo Flair. From visual experience, the following

1. In the Luminance enhancement part it has been shown that ETNUD works well for

2. In the contrast enhancement part it is clear that unseen or barely seen features of low

Good Regions 32 52 95 90 90 99

Poor Regions 68 48 5 10 10 1

γ

enhancement. IRME and MSRCR bring out the details in the dark but have some enhancement of noise in the dark regions, which can be considered objectionable. AINDANE does not bring out the finer details of the image. The ETNUD algorithm gives good result and outperforms the other algorithms if the results are compared (in Table 1) due to the Evaluation Criteria. The ETNUD provides better visibility

darker images and the technique adjusts itself to the image (Figure 2).

enhancement the best sharpness can be adjusted by the

Table 1. The Results of Evaluation Criteria for Figure 2.

1 /128 *<sup>n</sup>*

A region is considered to have sufficient contrast when 0.25 0.5. ≤ ≤

the region has sufficient sharpness. Image Quality is evaluated using by:

σ

σ

−

/128 64

*otherwise*

*<sup>n</sup>* ≤ An image is classified as GOOD when the total number of

(30)

(31)

= 1.4 does not provide good visual

parameter in Equation 19.

α

Image Gamma Irme Aindane Msr Etnud

 *<sup>n</sup>* When 0.25, σ*<sup>n</sup>* <

σ

*<sup>n</sup>* > the region has too much contrast.

 μ

*<sup>n</sup>* be normalized contrast parameter, such that:

σ

the region has poor contrast, and when 0.5,

and poor when 0.5.

**3.3 Experimental result** 

σ

regions classified as GOOD, 0.6 . *N N <sup>G</sup>* >

statements are made about the proposed algorithm:

contrast images are made visible. 3. In Figure 2 Gamma Correction with

Figure 2 Original

Number of

Number of

2. Let

σ

The ETNUD image enhancement algorithms provide high color accuracy and better balance between the luminance and contrast in images.
