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Satellite image enhancement and restoration – A review

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ISSN:2249-5789
S Maheshwari et al, International Journal of Computer Science & Communication Networks,Vol 6(4),198-204

Satellite Image Enhancement and Restoration – A Review
1

Mrs.S.Maheshwari, 2Dr.P.Krishnapriya
1
Assistant Professor, PG & Research Department of Computer Science
Dr.N.G.P Arts and Science College, Coimbatore-48
sivamahesh@gmail.com
2
Director - Department of Computer Applications, CIMAT, Coimbatore, India,
pkpriyaa@yahoo.com

Abstract
Satellite image processing is one of
the thrust areas in the field of computer
science research. Images taken by satellites
possibly degraded due to climate, weather
and other factors. Satellite image
enhancement and restoration is scientifically
possible by applying image processing and
other soft computing techniques. This review
paper presents various techniques applied in
satellite image enhancement and restoration.
Our review findings shows that there exists
lot of scope for performing satellite image
enhancement
and
restoration


using
amalgamating soft computing techniques and
conventional image processing. The
proposed doctoral research work is
descriptively portrayed in the paper.
Keywords: Satellite imagery, satellite image
processing, enhancement, restoration,
fusion.
1. Introduction
The purpose of image enhancement
and restoration techniques is to perk up a
quality and feature of a satellite image that
result in improved image than the original
one. There exist so many image enhancement
algorithms in the literatures that most often

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used techniques as global histogram
equalization
or
general
histogram
equalization
[1].
Usually
histogram
equalization technique alters the intensity
histogram in order to fairly accurate a

uniform distribution. The major pitfall of this
histogram equalization technique is the
problem of covering global image properties
that may not be appropriately applied in a
local context [2]. It is noteworthy that
histogram modification concerns with all
regions of the image equally that will result
in degraded local realization in terms of
detail conservation. In the same manner,
various local image enhancement algorithms
are also seen in the literatures. Enhancement
is a key step in the field of satellite image
processing. The assorted noises and relics in
imaging roles mortify the quality of the
satellite image. This paper reviews recent
literatures on satellite image enhancement
and restoration.
2. Related Works
Bhutada et al proposed [3] a novel
approach which utilizes features of wavelet
and curvelet transform, separately and
adaptively,
in
‘homogeneous’,
‘non-homogeneous’
and
‘neither

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ISSN:2249-5789
S Maheshwari et al, International Journal of Computer Science & Communication Networks,Vol 6(4),198-204

homogeneous
nor
non-homogeneous’
regions, which are identified by variance
approach. The edgy information that could
not be retained by wavelet approach is
extracted back from its residue by denoising
it with curvelet transform. This extracted
information is used as edge structure
information (ESI) for fusing offshore regions
of denoised images obtained by usage of
wavelet and curvelet transform. The result of
the image enhanced by such spatially
adaptive fusion technique shows the
improvement in the preservation of the edgy
information. It also yields better smoothness
in background (homogeneous region or
non-edgy region) due to the removal of fuzzy
edges developed during the denoising
process by the curvelet transform.
Yun Ling et al [4] have presented an
adaptive tone-preserved algorithm for image
detail enhancement in order to retain the
tonal distribution of the input image and
avoid experiential manipulation. Initially,
domain transform based multi-scale image

decomposition is carried out to quickly
divide the input image into a base image
which contains the coarse-scale image
information, and the detail layers which
contain the fine-scale details. Then, during
the process of detail enhancement and
synthesis, the authors constructed an
adaptive detail enhancement function based
on the edge response, to prevent the
exaggeration of strong edges and increase the
enhancing magnitude of small details.
Finally, in order to keep the color values of
the input image and the gradient values of the
detail enhanced image, a tonal correction
algorithm based on energy optimization is
presented to eliminate the distinct tonal

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differences of the enhanced image from the
input image. Their experimental results show
that
tone-consistent
image
detail
enhancement effect is available for arbitrary
input images with unified parameters setting,
which is superior to the state-of-the-art
methods.

In the study conducted by Kumar et al
[5], an improved multi-band satellite contrast
enhancement technique based on the singular
value decomposition (SVD) and discrete
cosine transform (DCT) was proposed for the
feature extraction of low-contrast satellite
images using normalized difference
vegetation index (NDVI) technique. Their
method employs multi-spectral remote
sensing data technique to find the spectral
signature of different objects such as the
vegetation
index
and
land
cover
classification presented in the satellite image.
Their proposed technique converts the image
into the SVD-DCT domain and after
normalizing the singular value matrix; the
enhanced image is reconstructed by using
inverse DCT. The visual and quantitative
results included in this study clearly show the
increased efficiency and flexibility of the
proposed method over the existing methods.
Their simulation results showed that the
enhancement-based NDVI using DCT-SVD
technique is highly useful to detect the
surface features of the visible area which are
extremely beneficial for municipal planning

and management.
In [6] Bhandari et al have presented
wavelet
filter
based
low contrast
multispectral
remote
sensing
image
enhancement by using singular value
decomposition (SVD). The input image is
decomposed into the four frequency

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ISSN:2249-5789
S Maheshwari et al, International Journal of Computer Science & Communication Networks,Vol 6(4),198-204

subbands through discrete wavelet transform
(DWT), and estimates the singular value
matrix of the low-low subband image and
then, it reconstructs the enhanced image by
applying inverse DWT. Their technique is
especially useful for enhancement of INSAT
as well as LANDSAT satellite images for
better feature extraction. The singular value
matrix represents the intensity information of
the given image, and any change on the

singular values changes the intensity of the
input image. Their proposed technique
converts the image into DWT-SVD domain
and after normalizing the singular value
matrix; the enhanced image is reconstructed
with the help of IDWT. The visual and
quantitative results clearly show the edge
sharpness,
increased
efficiency
and
flexibility of the proposed method based on
Meyer wavelet and SVD over the various
wavelet filters and also with exiting GHE
technique. Their experimental results (Mean,
Standard Deviation, MSE and PSNR)
derived from Meyer wavelet and SVD show
the superiority of the proposed method over
conventional methods.
Arici et al [7] proposed a general
framework based on histogram equalization
for image contrast enhancement. In their
framework, contrast enhancement is posed as
an optimization problem that minimizes a
cost function. The authors also stated that by
introducing specifically designed penalty
terms, the level of contrast enhancement can
be adjusted; noise robustness, white/black
stretching and mean-brightness preservation
may easily be incorporated into the

optimization. Analytic solutions for some of
the important criteria are presented. Finally, a
low-complexity algorithm for contrast

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enhancement is presented.
Bhandari et al [8] also proposed a
novel contrast enhancement technique for
contrast enhancement of a low-contrast
satellite image based on the singular value
decomposition (SVD) and discrete cosine
transform (DCT). The singular value matrix
represents the intensity information of the
given image and any change on the singular
values change the intensity of the input
image. Their proposed technique converts
the image into the SVD-DCT domain and
after normalizing the singular value matrix;
the enhanced image is reconstructed by using
inverse DCT. Their visual and quantitative
results suggested that their proposed
SVD-DCT method clearly shows the
increased efficiency and flexibility of their
proposed method over the exiting methods
such as the histogram equalization, gamma
correction and SVD-DWT based techniques.
In the process of satellite imaging, the
observed image is blurred by optical system

and atmospheric effects and corrupted by
additive noise. The image restoration method
known as Wiener deconvolution intervenes
to estimate from the degraded image an
image as close as possible to the original
image. The effectiveness of this method
obviously depends on the regularization term
which requires a priori knowledge of the
power spectral density of the original image
that is rarely, if ever, accessible, hence the
estimation of approximate values can affect
the restored image quality. In [9] Aouinti et
al came up with the idea consisted of
applying the genetic approach to the Wiener
deconvolution for satellite image restoration
through
the
optimization
of
this
regularization term in order to achieve the

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ISSN:2249-5789
S Maheshwari et al, International Journal of Computer Science & Communication Networks,Vol 6(4),198-204

best possible result.
Sajid and Khurshid [10] proposed

Recursive Least Square (RLS) adaptive
algorithm which is used for image restoration
from highly noise corrupted images. The
implementation
of
their
proposed
methodology is being carried out by
estimating the noise patterns of wireless
channel through configuring System
Identification with RLS adaptive algorithm.
Then, these estimated noise patterns are
eliminated
by
configuring
Signal
Enhancement with RLS algorithm. The
restored images are functioned for further
denoising and enhancement techniques.
Performance is evaluated by means of
Human Visual System, quantitative measures
in terms of MSE, RMSE, SNR & PSNR and
by graphical measures. Their experimental
results demonstrated that RLS adaptive
algorithm efficiently eliminated noise from
distorted images and delivered a virtuous
evaluation without abundant degradation in
performance.
Zhang and Man [11] have proposed a
satellite image adaptive restoration method

which avoids ringing artifacts at the image
boundary and retains oriented features. Their
method combines periodic plus smooth
image decomposition with complex wavelet
packet transforms. The framework first
decomposes a degraded satellite image into
the sum of a “periodic component” and a
“smooth component”. The Bayesian method
is then used to estimate the modulation
transfer function degradation parameters and
the noise. The periodic component is
deconvoluted using complex wavelet packet
transforms with the deconvolution result of
the periodic component then combined with

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the smooth component to get the final
recovered result. Their test results showed
that their strategy effectively avoids ringing
artifacts while preserving local image details.
Thriveni and Ramashri [12] proposed
a
DWT-PCA
based
fusion
and
Morphological gradient for enhancement of
Satellite images. The input image is

decomposed into different sub bands through
DWT. PCA based fusion is apply on the
low-low sub band, and input image for
contrast enhancement. IDWT is used to
reconstructs the enhanced image. To achieve
sharper boundary discontinuities of image,
an intermediate stage estimating the fine
detail sub bands is required. This has been
done by the success of threshold
decomposition, morphological gradient
based operators are used to detect the
locations of the edges and sharpen the
detected edges. Their proposed method has
been shown that improved visibility and
perceptibility of various digital satellite
images.
Aedla [13] et al have presented a new
contrast enhancement technique for satellite
images based on clipping or plateau
histogram equalization. Their technique
adopted Bi-Histogram Equalization with
Plateau Limit (BHEPL) for image
decomposition and Self-Adaptive Plateau
Histogram Equalization (SAPHE) for
threshold calculation. Their proposed method
has been compared with existing methods
such as Histogram Equalization (HE),
Brightness
Preserving
Bi-Histogram

Equalization (BBHE), Minimum Mean
Brightness Error Bi-Histogram Equalization
(MMBEBHE),
Dynamic
Histogram
Equalization
(DHE),
Bi-Histogram

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ISSN:2249-5789
S Maheshwari et al, International Journal of Computer Science & Communication Networks,Vol 6(4),198-204

Equalization with Plateau Limit (BHEPL)
and Self-Adaptive Plateau Histogram
Equalization (SAPHE) with image quality
measures such as Absolute Mean Brightness
Error (AMBE) and Peak-Signal to Noise
Ratio (PSNR).

inverse DWT. Their technique is applied to
grey level, colour image and satellite image
and their comparative analysis were done.
Their experimental results showed the
superiority of their proposed method over
conventional techniques.

Soni et al [14] proposed an improved

method based on evolutionary algorithms for
denoising of satellite images. In their
approach, the stochastic global optimisation
techniques such as Cuckoo Search (CS)
algorithm, artificial bee colony (ABC), and
particle swarm optimisation (PSO) technique
and their different variants are exploited for
learning the parameters of adaptive
thresholding function required for optimum
performance. It was found that the CS
algorithm and ABC algorithm-based
denoising approach gave better performance
in terms of edge preservation index or edge
keeping index (EPI or EKI) peak
signal-to-noise
ratio
(PSNR)
and
signal-to-noise ratio (SNR) as compared to
PSO-based denoising approach. Their
proposed technique has been tested on
satellite images. The quantitative (EPI,
PSNR and SNR) and visual (denoised
images) results show superiority of the
proposed technique over conventional and
state-of-the-art image denoising techniques.

3. Proposed Research Work

Bidwai and Tuptewar [15] have

developed a method to enhance the quality of
image. The enhancement is done both with
respect to resolution as well as contrast. Their
proposed technique uses DWT and SVD.
Their technique decomposes the input image
into four sub-bands by using DWT and
estimates singular value matrix of low
frequency sub-band image, then it
reconstructs enhanced image by applying

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3.1. Phase - 1
In this initial phase we aim to remove
noise from image using enhanced filtering
technique. The proposed enhanced filter is
applied to the satellite images that are
affected by Gaussian noise and the filter is
applied images affected by impulse noise
(salt and pepper noise). Using the proposed
filtering algorithm, the Gaussian noise is
removed and the satellite image is input to
the p (i, j). Then an amalgam filter is
developed by combining two filters (adaptive
median filter and p (i, j) that are affected by
Gaussian noise, and a noise free image is
undergone the restoration process. The image
I noise is removed and a noise-free output
image is obtained. Here, the bilateral filter

performance is improved by optimizing the
two parameters that control the filter
behavior. p(i, j) affected by the impulse noise
is denoised by applying the optimized
bilateral filter.
3.2. Phase - 2
This phase is aimed to develop a
method to extract unique features from
satellite
image
based
on
Hough
Transformation and Local Binary Patterns.
The Hough Transformation method is
employed in order to highlight linear
features. It is assumed, however, that
applying this method by using an LBP
operator that supports the features of the

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ISSN:2249-5789
S Maheshwari et al, International Journal of Computer Science & Communication Networks,Vol 6(4),198-204

texture associated with rectitude will
improve results. The contribution of this
work is the LBP texture operator that
introduces criteria, added to the image

radiometry to improve extraction. This
ensures convergence to an optimal solution
while controlling the contextual information.
The performance of this method will be
verified using satellite images. It is to be
noted that proposed system is aimed to be an
effective proposal for application to satellite
images at medium and high resolution.

DWT-PCA based fusion and morphological
gradient, periodic plus smooth image
decomposition and complex wavelet packet
transforms, RLS adaptive filter and
enhancement, genetic approach to the
Wiener deconvolution, discrete cosine
transform and SVD were reviewed. Our
findings shows that there exists lot of scope
for performing satellite image enhancement
and restoration using amalgamating soft
computing techniques and conventional
image processing.

3.3. Phase - 3
In this phase of research, quality of
the satellite image is aimed to be improved
where criteria including removal of
illumination color cast and information
content increment are to be met. Genetic
algorithm is chosen for accomplishing this
goal. In GA the Pareto front concept is

adopted that converts the conventional GA
into the multi-objective genetic algorithm
(MOGA). The proposed MOGA for image
enhancement contains several components
that includes initialization, iteration with
objective evaluation, determination of the
Pareto front and its management, update. In
particular the color correction process and
the assignment of the qualities in color
correction along with information gain are
given priority.
4. Conclusions
In this paper several mechanisms
which includes improved sub-band adaptive
Thresholding function based on evolutionary
algorithms,
Satellite
image
contrast
enhancement algorithm based on plateau
histogram equalization, edge preserving
Satellite
image
enhancement
using

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References

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Processing” 3rd ed. Englewood Cliffs, NJ:
Prentice-Hall; 2007.
[2] Tang J, Peli E, Acton S, “Image enhancement
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IEEE Signal Processing Letters Vol.10, No.4, 2003,
pp.289–92.
[3] G.G.Bhutada, R.S.Anand, S.C.Saxena, "Edge
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ISSN:2249-5789
S Maheshwari et al, International Journal of Computer Science & Communication Networks,Vol 6(4),198-204

[7] T. Arici, S. Dikbas and Y. Altunbasak, "A
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