IRJET- Satellite Image Resolution Enhancement using Dual-tree Complex Wavelet Transform and Non Local Mean

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 04 | Apr-2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 220
Satellite Image Resolution Enhancement using Dual-tree Complex
Wavelet Transform and Non Local Mean
Dhiraj Nehate 1, Prof. P.A. Salunkhe 2
1 PG student, Electronics and Telecommunications, Mumbai University, Mumbai, India
2 Professor, Electronics and Telecommunications, Mumbai University, Mumbai, India
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Abstract - Resolution enhancement (RE) schemes (which
are not based on wavelets) suffer from the Disadvantage of
losing high-frequency contents (which results in blurring).
The discrete-wavelet-transform-based (DWT) RE scheme
generates artifacts (due to a DWT shift-variant property).
A wavelet-domain approach based on dual-tree complex
wavelet transform (DT-CWT) and nonlocal means (NLM) is
proposed for RE of the satellite images. A satellite input
image is decomposed by DT-CWT (which is nearly shift
invariant) to obtain high-frequency sub-bands. The high-
frequency and the low-resolution (LR) input image are
interpolated using the Lanczos interpolator. The high-
frequency sub-bands are passed through an NLM Filter to
cater for the artifacts generated by DT-CWT (despite of
it’s nearly shift invariance). The Fi ltered high-frequency
sub-bands and the LR input image are combined using
inverse DT-CWT to obtain a resolution-enhanced image.
Objective and subjective analyses reveal superiority of the
proposed technique over the conventional and state-of-the-art
RE techniques.
Key Words: Dual-tree complex wavelet transform (DT-
CWT), Lanczos interpolation, resolution enhancement
(RE), shift-variant
1. INTRODUCTION
In the Recent years there is increased in the
demand for best quality images in the various
applications such as medical, astronomy, object
recognition. Satellite images are used in diverse areas
such as monitoring the processes on the Earth’ssurface,
discovery of changes in atmosphere; measuring as
well as estimating geographical, biological and physical
parameters, etc. The resolution of these images is
extremely significant to obtain information from
satellite images so it plays a main role in satellite image
enhancement. And the Image Enhancement is a process
of obtaining a high quality or high resolution image
from low quality otherwise low resolution satellite
image, for supplementary processing of an image, such
as analysis, detection, segmentation along with
recognition [2]. It is an essential step in image
processing of satellite images. Image resolution
enhancement is also widely useful for satellite image
applications which contain bridge recognition, building
construction in GPS technique. For image
enhancement method there are two domains has
been occupied into consideration one is image domain
as well as transform domain. Transform domain
conclude which transformations used in the
Enhancement. Image interpolation is usually used
resolution enhancement scheme for different
applications. Image interpolation is the process of
using recognized more data to approximation values
at unknown locations. Interpolation method select
new pixel from surrounding pixels. Mostly there
are two types of interpolation algorithms.
1. Adaptive algorithm- This algorithm changes
depending on what they are interpolating.
2. Non adaptive algorithms- contain linear
interpolation algorithms
Linear interpolation includes Adjacent, bilinear,bicubic
interpolation. But images obtained by these linear
interpolation technique produces numerous artifacts
similar to blurring, blocking etc. To avoid these problems
non linear interpolation algorithms are intended for
Resolution Enhancement.
1.1 Dual-tree Complex Wavelet Transform
This method, dual-tree CWT (DT-CWT) [4] [8] is used
to decompose an input image into different sub-band
images. In this method direction selective filters are used
to generate high frequency sub-band images where
filter demonstrate peak magnitude responses in the
existence of image features oriented at angle +75,
+45, +15, -15, -45 and -75 degrees, respectively [9].
Subsequently six complex valued images are
interpolated. Once interpolated, combine all images to
create a new high-resolution image by using
inverse DT-CWT. Resolution is achieved [8] by using
directional selectivity provided with the CWT, where
the high-frequency subbands contribute to the
sharpness of the high-frequency details. Finally IDT-
CWT used to join all these images to construct
resolution enhanced image.

Fig 1.Block Diagram of DT CWT
Where m is the frame index, moreover N represents
the neighborhood of the pixel at location (p, q).
K values are the filter weights, i.e.,
K(r, s) = exp
* ….(2)
Where V is the window [typically a square window
centered on the pixels Y (p, q) with Y (r, s)] of pixel
values from a geometric neighborhood of pixels Y (p, q)
as well as Y (r, s), σ is the filter coefficient, f (.) is a
geometric distance function. K is inversely proportional
to the distance between Y (p, q) and Y (r, s).
1.2 NLM Filtering
The NLM filter which is an extension of
neighborhood filtering algorithms and it is based on
the assumption that image content is likely to
replicate itself within some neighborhood and in
neighboring frame. It computes de-noised x (p, q) with
the weighted sum of the adjacent pixels of Y (p, q)
(within frame and in the neighboring frames). This
characteristic provides a way to estimate the pixel value
from noise contaminated images. In a 3-D NLM
algorithm, the estimate of a pixel at point (p, q) is
2. Flow of proposed technique
Input Image:-
Satellite Input Image Is Capture from Satellite Imaging
Corporation Web Page .Satellite is Low Resolution Image
Preprocessing:-
In processing the resize the image into low resolution Input
image (128*128)
Multilevel Dual Tree Complex Wavelet Transform:-
The two levels Dual Tree Complex Wavelet Transform are
used. In multilevel DT-CWT decomposed the low resolution
input image in diff.sub band .the sub-bands separated into
image coefficient & Wavelet coefficient sub band. The12sub
band are produce by multi level DTCWT .2 D DWT are used
King Q filter DTCWT:-
Q filter improved the Orthogonality & Symmetric Properties
of the filter bank. Analysis & analysis Filter

Fig.2 Block diagram of the DT-CWT RE Algorithm.
Lanczos Interpolation:-
Lanczos Interpolation function in 2D & low resolution input
image is interpolated. Lanczos used to resizing the image.
For Lanczos re-sampling and lanczos filtering, lowpassfilter
used smoothen interpolated the value of the digital signal
between sample.
Non local mean:-
Non local means is the algorithm in the image processingfor
image denoising.non local mean filtering takes a mean of
every pixel in that image , weighted by how similar this are
pixel are to the target pixel.12 sub-band are filter filtered &
interpolated
Inverse DTCWT:
In Inverse DTCWT are combine the high frequency sub-
band & low frequency Sub-band. Then resize 128*128
produced the high resolution Image
Resize & Resolution image:-
Then resize 128*128 produced the high resolution Image
3. Flowchart of Proposed Technique (DT-CWT NLM) :
Figure 3: Flowchart of the proposed Method.

To estimate the performance of proposed technique
algorithm different metrics such as, Mean Square Error
(MSE), Peak Signal to. Noise Ratio (PSNR) has been
calculated.
4. Experiments and Result Analysis:
Result – (A) Image Resolution using DT-CWT NLM RE
Technique :
Fig4.Input Image1 Fig5.Output Image1
Fig6. Input Image2 Fig7.output Image2
Fig8.Input Image 3 Fig9.output Image 3
Experimental Results:
Proposed DT-CWT-NLM-RE Technique(mathematical
paramdeter)
Test Image MSE PSNR(dB)
Image 1 0.0182 17.40
Image 2 0.0176 17.55
Image 3 0.0197 17.06
Table I
 The Results obtained by proposed technique DT-
CWT-NLM-RE are much better than another
technique
 Table I. shows that in the proposed technique
provide better result in the term of MSE,PSNR
5. CONCLUSIONS
An RE technique based on DT-CWT as well as an NLM
filter has been proposed. Wavelet coefficients and the
LR input image were interpolated using the Lanczos
interpolator & The NLM filtering is used to overcome the
artifact generated by DT-CWT & to enhancetheperformance
of proposed technique in the term of MSE & PSNR &
simulation results highlight the performance of proposed
technique. In view of the above discussion the proposed
system can be one of the best image resolutionenhancement
Technique.
ACKNOWLEDGEMENT
The authors would like to thank satellite Imaging
Corporation for providing satellite image for research
purpose
REFERENCES
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8. [Online].Available:http://www.satimagingcorp.
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