06-02-2013, 04:55 PM
Satellite Image Contrast Enhancement Using Discrete Wavelet Transform and Singular Value Decomposition
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Abstract—
In this letter, a new satellite image contrast enhancement
technique based on the discrete wavelet transform (DWT)
and singular value decomposition has been proposed. The technique
decomposes the input image into the four frequency subbands
by using DWT and estimates the singular value matrix of
the low–low subband image, and, then, it reconstructs the enhanced
image by applying inverse DWT. The technique is compared
with conventional image equalization techniques such as
standard general histogram equalization and local histogram
equalization, as well as state-of-the-art techniques such as brightness
preserving dynamic histogram equalization and singular
value equalization. The experimental results show the superiority
of the proposed method over conventional and state-of-the-art
techniques.
INTRODUCTION
SATELLITE images are used in many applications such as
geosciences studies, astronomy, and geographical information
systems. One of the most important quality factors in
satellite images comes from its contrast. Contrast enhancement
is frequently referred to as one of the most important issues
in image processing. Contrast is created by the difference
in luminance reflected from two adjacent surfaces. In visual
perception, contrast is determined by the difference in the color
and brightness of an object with other objects. Our visual
system is more sensitive to contrast than absolute luminance;
therefore, we can perceive the world similarly regardless of the
considerable changes in illumination conditions.
PROPOSED IMAGE CONTRAST ENHANCEMENT
There are two significant parts of the proposed method.
The first one is the use of SVD. As it was mentioned, the
singular value matrix obtained by SVD contains the illumination
information. Therefore, changing the singular values will
directly affect the illumination of the image; hence, the other
information in the image will not be changed. The second
important aspect of this work is the application of DWT. As
it was mentioned in Section I, the illumination information is
embedded in the LL subband. The edges are concentrated in
the other subbands (i.e., LH, HL, and HH). Hence, separating
the high-frequency subbands and applying the illumination
enhancement in the LL subband only will protect the edge
information from possible degradation. After reconstructing
the final image by using IDWT, the resultant image will not
only be enhanced with respect to illumination but also will be
sharper.
CONCLUSION
In this letter, a new satellite image contrast enhancement
technique based on DWT and SVD was proposed. The proposed
technique decomposed the input image into the DWT
subbands, and, after updating the singular value matrix of the
LL subband, it reconstructed the image by using IDWT. The
technique was compared with the GHE, LHE, BPDHE, and
SVE techniques. The visual results on the final image quality
show the superiority of the proposed method over the conventional
and the state-of-the-art techniques. The authors would
like to thank H. Ibrahim and N. S. P. Kong from the School
of Electrical and Electronic Engineering, University Sains
Malaysia, for providing the equalized output images of the
BPDHE technique and also Satellite Imaging Corporation, AutomaticWeather
Stations Project, and Antarctic Meteorological
Research Centre for providing the satellite images for research
purposes.