18-07-2012, 03:53 PM
An image contrast enhancement method based on genetic algorithm
An image contrast enhancement.pdf (Size: 1.91 MB / Downloads: 86)
abstract
Contrast enhancement plays a fundamental role in image/video processing. Histogram Equalization (HE)
is one of the most commonly used methods for image contrast enhancement. However, HE and most
other contrast enhancement methods may produce un-natural looking images and the images obtained
by these methods are not desirable in applications such as consumer electronic products where brightness
preservation is necessary to avoid annoying artifacts. To solve such problems, we proposed an efficient
contrast enhancement method based on genetic algorithm in this paper. The proposed method uses
a simple and novel chromosome representation together with corresponding operators. Experimental
results showed that this method makes natural looking images especially when the dynamic range of
input image is high. Also, it has been shown by simulation results that the proposed genetic method
had better results than related ones in terms of contrast and detail enhancement and the resulted images
were suitable for consumer electronic products.
Introduction
Contrast enhancement is a process that is applied on images or
videos to increase their dynamic range. Since now, many algorithms
have been proposed for such an aim. Histogram Equalization
(HE) is one of the most commonly used method for contrast
enhancement (Gonzalez and Woods, 2008; Jain, 1989; Zimmerman
et al., 1988; Kim, 1997; Kim et al., 1998). It is a simple method and
has been used in various fields such as medical image processing
and texture analysis (Pei et al., 2004; Wahab et al., 1998; de la
Torre et al., 2005; Pizer, 2003). The main objective of this method
is to achieve a uniform distributed histogram by using the cumulative
density function of the input image (Chen and Ramli, 2003). It
has been shown that the mean brightness of the histogram-equalized
image is the middle gray level of the input image regardless of
its mean (Chen and Ramli, 2003). This is not a suitable property in
some applications such as consumer electronic products, where
brightness preservation is necessary to avoid annoying artifacts
(Chen and Ramli, 2003).
Proposed genetic method
In this section, the proposed genetic algorithm for image contrast
enhancement is described.
Chromosome structure
This method uses a simple chromosome structure. An example
of the chromosome structure has been shown in Fig. 1. This structure
uses a sorted array of random integer numbers. The size of
each chromosome is equal to n, where n represents the number
of gray levels in the input image. In the proposed structure, the
indices indicate the order of gray levels in the image, for example
the index 1 indicates the first gray level in the image and so on. In
Fig. 1, the first gray level in the image is 0, the second one is 25, the
third one is 40, and the last one is 255. In remapping, the first gray
level in original image is replaced with the value of first gen of
chromosome and so on.
Experimental results
In this section, at first the structure of proposed method is compared
to some genetic based methods. Table 1 shows this comparison.
As it is shown in Table 1, the main difference between
proposed method and related ones is in chromosome
representation.
To demonstrate the performance of the proposed algorithm, the
presented method was implemented by Matlab on PC computer
with 1.6 GHZ CPU and 1 GB RAM. Also, some 256 * 256 bench mark
images were used to show the performance of the proposed method.
The applied parameter values in simulation have been shown
in Table 2. Also, some other related methods have been implemented
and their results were compared with proposed method.
The comparison has been done in terms of ability in contrast and
detail enhancement, appropriateness of enhanced images for consumer
electronic products and ability of the proposed method to
produce natural looking images.
Conclusion
In this paper, we proposed a genetic based method for image
contrast enhancement especially when input image has low dynamic
range. The proposed method is based on a simple chromosome
structure and overcomes the previous methods shortcomings. To confirm the method performance, some standard
bench mark images were selected and the proposed method was
applied on them. The experimental results were satisfactory. Also,
to compare the proposed method with other related ones, three
different criteria have been used: number of detected edges, PSNR
and visual assessment. The proposed method was better than related
ones in most cases. Besides, experiment results demonstrated
that the enhanced images are suitable for applications such as consumer electronic products.