22-06-2012, 02:37 PM
Minimum Mean Brightness Error Bi-Histogram Equalization in
Contrast Enhancement
[attachment=25086]
INTRODUCTION
Histogram equalization (HE) is a very popular technique for
enhancing the contrast of an image [1]. Its basic idea lies on
mapping the gray levels based on the probability distribution
of the input gray levels. It flattens and stretches the dynamics
range of the image's histogram and resulting in overall contrast
improvement. HE has been applied in various fields such as
medical image processing and radar image processing [2].
Nevertheless, HE is not commonly used in consumer
electronics such as TV because it may significantly change the
brightness of an input image and cause undesirable artifacts.
MINIMUM MEAN BRIGHTNESS ERROR BI-HISTOGRAM EQUALIZATION (MMBEBHE)
Based on the above discussion, MMBEBHE is formally
defined by the following procedures:
1. Calculate the AMBE for each of the threshold level.
2. Find the threshold level, XT that yield minimum MBE,
3. Separate the input histogram into two based on the XT found
in step 2 and equalized them independently as in BBHE
Step 2 and 3 are straightforward process.
CONCLUSION
In this paper, a new contrast enhancement algorithm
referred as the Minimum Mean Brightness Error Bi-Histogram
Equalization (MMBEBHE) with better brightness preservation
is proposed. The MMBEBHE is a novel extension of BBHE.
The main idea lies on separating the histogram using the
threshold level that would yield minimum Absolute Mean
Brightness Error (AMBE). The ultimate goal behind the
MMBEBHE is to allows maximum level of brightness
preservation in Bi-Histogram Equalization to avoid unplesant
artifacts and unnatural enhancement due to excessive
equalization while enhancing the constrast of a given image as
much as possible. This paper has also formulated an efficient,
recursive and integer-based solution to approximate the output
mean as function of threshold level. Simulation results using
sample image which represent images with very low, very high
and medium mean brightness have demonstrated that the cases
which are not handled well by HE, BBHE and DSIHE, can be
properly enhanced by MMBEBHE. Besides, MMBEBHE also
demostrate comparable performance with BBHE and DSIHE
when come to use the sample images shown in [2] and [3].
Contrast Enhancement
[attachment=25086]
INTRODUCTION
Histogram equalization (HE) is a very popular technique for
enhancing the contrast of an image [1]. Its basic idea lies on
mapping the gray levels based on the probability distribution
of the input gray levels. It flattens and stretches the dynamics
range of the image's histogram and resulting in overall contrast
improvement. HE has been applied in various fields such as
medical image processing and radar image processing [2].
Nevertheless, HE is not commonly used in consumer
electronics such as TV because it may significantly change the
brightness of an input image and cause undesirable artifacts.
MINIMUM MEAN BRIGHTNESS ERROR BI-HISTOGRAM EQUALIZATION (MMBEBHE)
Based on the above discussion, MMBEBHE is formally
defined by the following procedures:
1. Calculate the AMBE for each of the threshold level.
2. Find the threshold level, XT that yield minimum MBE,
3. Separate the input histogram into two based on the XT found
in step 2 and equalized them independently as in BBHE
Step 2 and 3 are straightforward process.
CONCLUSION
In this paper, a new contrast enhancement algorithm
referred as the Minimum Mean Brightness Error Bi-Histogram
Equalization (MMBEBHE) with better brightness preservation
is proposed. The MMBEBHE is a novel extension of BBHE.
The main idea lies on separating the histogram using the
threshold level that would yield minimum Absolute Mean
Brightness Error (AMBE). The ultimate goal behind the
MMBEBHE is to allows maximum level of brightness
preservation in Bi-Histogram Equalization to avoid unplesant
artifacts and unnatural enhancement due to excessive
equalization while enhancing the constrast of a given image as
much as possible. This paper has also formulated an efficient,
recursive and integer-based solution to approximate the output
mean as function of threshold level. Simulation results using
sample image which represent images with very low, very high
and medium mean brightness have demonstrated that the cases
which are not handled well by HE, BBHE and DSIHE, can be
properly enhanced by MMBEBHE. Besides, MMBEBHE also
demostrate comparable performance with BBHE and DSIHE
when come to use the sample images shown in [2] and [3].