satellite image resolution enhancement

8/3/2019 Satellite Image Resolution Enhancement

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1.INTRODUCTION

Nowadays, satellite images are used in many applications such as geoscience

studies, astronomy, and geographical information systems. One of the most

important quality factors in images comes from its resolution. Interpolation in

image processing is a well-known method to increase the resolution of a digital

image. Interpolation has been widely used in many image-processing applications

such as facial reconstruction, multiple-description coding, and resolution

enhancement.

Many interpolation techniques have been developed to increase the image

resolution. There are three well-known interpolation techniques, namely, nearest

neighbor interpolation, bilinear interpolation, and bicubic interpolation. Bicubic

interpolation is more sophisticated than the other two techniques; however, it

produces noticeably sharper images.

Image-resolution enhancement in the wavelet domain is a relatively new

research topic, and, recently, many new algorithms have been proposed. Complex

wavelet trans-form (CWT) is one of the recent wavelet transforms used in image

processing. A one-level CWT of an image produces two complex-valued low-

frequency subband images and six complex-valued high-frequency subband

images. The high frequency subband images are the result of direction-selective

filters. They show peak magnitude responses in the presence of image features

oriented at +75_, +45_, +15_, 15_, 45_, and75_.


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We propose a new technique which generates sharper and detailed super

resolved satellite images. The proposed technique uses dual-tree CWT (DT-CWT)

to decompose a low-resolution image into different subband images. Then the six

complex-valued high-frequency subband images are interpolated using bicubic

interpolation. In parallel, the input image is also interpolated separately. Finally,

the interpolated high-frequency subband images and interpolated input image are

combined by using inverse DT-CWT (IDT-CWT) to achieve a high-resolution

output image. The proposed technique has been compared with conventional and

state-of-the-art image resolution enhancement techniques.


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WA

The integral wavelet

The wavelet coefficients cjk

Here, a = 2j is called the

binary or dyadic position.

`Wavelet compre

Wavelet compressio compression (sometimes al

implementations are JPEG

the BBC's Dirac, and Ogg

little space as possible in

lossy.

Using a wavelet tran

for representing transients,

components in two-dimensi

sky. This means that the tra

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ELET TRANSFORM

ransform is the integral transform define

are given by:

binary dilation or dyadic dilation, and

ssion

n is a form of data compression well suo video compression and audio compre

000, DjVu and ECW for still images, a

arkin for video. The goal is to store i

file. Wavelet compression can be eit

sform, the wavelet compression method

such as percussion sounds in audio, or

onal images, for example an image of s

sient elements of a data signal can be re

d as:

= k2j is the

ited for imagesion). Notable

d REDCODE,

age data in as

er lossless or

s are adequate

igh-frequency

ars on a night

resented by a


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smaller amount of information than would be the case if some other transform,

such as the more widespread discrete cosine transform, had been used.

Wavelet compression is not good for all kinds of data: transient signal

characteristics mean good wavelet compression, while smooth, periodic signals are

better compressed by other methods, particularly traditional harmonic compression

(frequency domain, as by Fourier transforms and related). Data statistically

indistinguishable from random noise is not compressible by any means.

METHOD

First a wavelet transform is applied. This produces as many coefficients as

there are pixels in the image (i.e.: there is no compression yet since it is only a

transform). These coefficients can then be compressed more easily because the

information is statistically concentrated in just a few coefficients. This principle is

called transform coding. After that, the coefficients are quantized and the quantized

values are entropy encoded and/or run length encoded.

A few 1D and 2D applications of wavelet compression use a technique

called "wavelet footprints".


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An example of the 2D discrete wavelet transform that is used in JPEG2000.

EXISTING TECHNOLOGIES

The existing technologies for super resolved images include the following:

1) Interpolation techniques, namely, nearest interpolation, bilinear interpolation,

and bicubic interpolation;

2) Wavelet zero padding (WZP);

3) single-frame resolution enhancement by Irani and Peleg where four low

resolution images generated by rotation and translation from the input low

resolution image are used.


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PROPOSED TECHNOLOGY

Resolution is an important feature in satellite imaging, which makes the

resolution enhancement of such images to be of vital importance. As it was

mentioned before, there are many applications of using satellite images; hence,

resolution enhancement of such images will increase the quality of the other

applications.

The main loss of an image after being super resolved by applying

interpolation is on its high-frequency components (i.e., edges), which is due to the

smoothing caused by interpolation. Hence, in order to increase the quality of the

super resolved image, preserving the edges is essential.

In this proposed technology DT-CWT has been employed in order to preserve the

high-frequency components of the image. The DT-CWT has good directional

selectivity and has the advantage over discreet wavelet transform (DWT). It also

has limited redundancy. The DT-CWT is approximately shift invariant, unlike the

critically sampled DWT. The redundancy and shift invariance of the DT-CWT

mean that DT-CWT coefficients are inherently interpolable.

In the proposed image resolution enhancement technique, DT-CWT is used

to decompose an input image into different subband images. Six complex-valued

high-frequency subband images contain the high-frequency components of the

input image. In the proposed technique, the interpolation is applied to the high-

frequency subband images. In the wavelet domain, the low-resolution image is

obtained by lowpass filtering of the high-resolution image. In other words, low-

frequency subband images are the low resolution of the original image.


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Basically the steps involved are the following

1. The dual-tree CWT (DT-CWT) decomposes a low-resolution image into

different sub band images.

2. The sub band images are interpolated using bi cubic interpolation.

3. In parallel, the input image is also interpolated separately.

4. Finally, the interpolated high-frequency sub band images and interpolated

input image are combined by using inverse DT-CWT (IDT-CWT)

Block diagram of the proposed resolution enhancement algorithm.


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BLOCK DIAGRAM EXPLANATION

DT-CWT is used to decompose an input image into different subband

images. Six complex-valued high-frequency subband images contain the high-

frequency components of the input image. In the proposed technique, the

interpolation is applied to the high-frequency subband images. In the wavelet

domain, the low-resolution image is obtained by lowpass filtering of the high-

resolution image. In other words, low-frequency subband images are the low

resolution of the original image.

Therefore, instead of using low-frequency subband images, which contain

less information than the original input image, we are using the input image for the

interpolation of two low-frequency subband images. Hence, using the input image

instead of the low-frequency subband images increases the quality of the super

resolved image. The input image is interpolated with the half of the interpolation

factor used to interpolate the high-frequency subbands. The two upscaled imagesare generated by interpolating the low-resolution original input image and the

shifted version of the input image in horizontal and vertical directions. These two

real-valued images are used as the real and imaginary components of the

interpolated complex LL image for the IDT-CWT operation.

By interpolating the input image by /2 and the high-frequency subband

images by and then by applying IDT-CWT, the output image will contain sharper

edges than the interpolated image obtained by interpolation of the input image

directly.


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This is due to the fact that the interpolation of the isolated high-frequency

components in the high-frequency subband images will preserve more high-

frequency components after the interpolation of the respective subbands separately

than interpolating the input image directly.

In summary, the proposed technique interpolates not only the input image

but also the high-frequency subband images obtained through the DT-CWT

process. The final high-resolution output image is generated by using the IDTCWT

of the interpolated subband images and the input image. In the proposed technique,

the employed interpolation method is the same for all subband and the input

images. The visual and the peak signal-to-noise ratio (PSNR) results, in the next

section, show that the proposed technique with bicubic interpolation outperforms

the conventional bicubic interpolation, WZP and Irani and Peleg techniques.


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DT-CWT TECHNIQUE

A one-level CWT of an image produces two complex- valued low-frequency

sub band images and six complex-valued high-frequency sub band images.

The high frequency Sub band images are the result of direction-selective

filters i.e. when input signal is passed through these filters they select specific

directions according to the filter design.

They show peak magnitude responses in the presence of image featuresoriented at +75 , +45, +15, 15, 45 and 75 .

DT-CWT is used to decompose an input image into different sub band images.

The resolution enhancement is achieved by using directional selectivity

provided by the CWT where the high-frequency sub bands in six different

directions contribute to the sharpness of the high-frequency details such as edges.

The main loss of an image after being super resolved by applying

interpolation is on its high-frequency components. This is due to the smoothing

cause by interpolation. So preserving the edges is essential.

DT-CWT has been employed in order to preserve the high-frequency

components of the image. DT-CWT has good directional selectivity.


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COMPARISON

PSNR (dB) results for the proposed resolution enhancement with a

factor of two, from 256 256 to 512 512, for the input images shown

in fig. 2(a) compared with the conventional and state-of-the-art image

resolution enhancement techniques

ADVANTAGES

The DT-CWT has good directional selectivity

Advantage over discrete wavelet transforms (DWT).

Limited redundancy.

The DT-CWT is approximately shift invariant, unlike the critically sampled

DWT.

DT-CWT coefficients are inherently interpolable.


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RESULTS

Super resolved images using the proposed technique in (c) are much sharper

than the original low-resolution images in (a) and the interpolated images in (b).

In order to show the effectiveness of the proposed method over the

conventional and state-of-the-art image resolution enhancement techniques, five

satellite images with different features are used for comparison.

In the published literature, basically two groups of approaches can be found.

Approaches focusing on radiometric correctness use formulas in closed form for

providing intensity values of high quality that are comparable to real SAR images

While the level of detail of 3-D models to be used is limited.


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SATELLITE IMAGES

Example no:1

a) Original low-resolution image b) Bi cubic interpolated image.

c) Super resolved image using the proposed technique.


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example no:2 a)

b)

c)


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CONCLUSION

A satellite image resolution enhancement technique based on the

interpolation of the high-frequency subband images obtained by DT-CWT and the

input image. The proposed technique uses DT-CWT to decompose an image into

different subband images, and then the high-frequency subband images are

interpolated. An original image is interpolated with half of the interpolation factor

used for interpolation of the high-frequency subband images. Afterward, all these

images are combined using IDT-CWT to generate a superresolved image. The

proposed technique has been tested on several satellite images, where their PSNR

and visual results show the superiority of the proposed technique over the

conventional and state-of-the-art image resolution enhancement techniques. The

main reason of the resolution enhancement can be attributed to the directional

selectivity of the CWT, where the high frequency subbands in six different

directions contribute to the sharpness of the high-frequency details such as edges..


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