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Title : Satellite Image Enhancement using Fast Discrete Curvelet TransformAuthor (s) :Mohammed Abdulwahhab Ahmed, E. Sreenivasa Reddy

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ISSN (Online): 2348 – 3539

Satellite Image Enhancement using Fast Discrete Curvelet Transform

1

Mohammed Abdulwahhab Ahmed,

2

E. Sreenivasa Reddy

University College of Engineering and Technology, Acharya Nagarjuna University, Guntur.

Abstract: 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. If the contrast of an image is highly concentrated on a specific range, the information may be lost in those areas which are excessively and uniformly concentrated. The problem is to optimize the contrast of an image in order to represent all the information in the input image. A new satellite image contrast and resolution enhancement technique based on the discrete curvelet transform (DCT) with singular value decomposition on tophat transform has been proposed. In this work the technique decomposes the input eight sub bands by using discrete curvelet transform (DCT) and estimates the singular value matrix of the low–low sub band image, and, then, it reconstructs the enhanced image by applying inverse DCT. The technique is compared with DWT using SVD on Morphological Process on Colour Images and Gray Scale Images. This method will give better qualitative and quantitative results on Root Mean Square Error and Peak-Signal Noise Ratio.

Keywords: RGB Component, FDCT,TOPAHT, Structure Element.

Reference to this paper should be made as follows: 1Mohammed Abdulwahhab Ahmed,2E. Sreenivasa Reddy (2016) „Satellite Image Enhancement using Fast Discrete Curvelet Transform ‟, International Journal of Inventions in Computer Science and Engineering, Volume 2 Issue 5 May 201 .

1 Introduction

In the modern information system, digital images have been widely used in a growing number of applications. The effort on edge enhancement has been focused mostly on improving the visual perception of images that are unclear because of blur. In general, the popular edge enhancement filtering is carried out with the help of traditional filters [1, 2 and 3]. But these filters do have some problems, Noise removal and preservation of useful us information are important aspects of image enhancement. A wide variety of methods have been proposed to solve the edge preserving and noise removal problem. Recently, researchers have focused their attention on nonlinear smoothing techniques in the spatial domain. Most of these techniques are local smoothing filters, which replace the center pixel of the neighborhood by an average of selected neighbor pixels. Mainly focusing on the clarity of the image and the number of computations done for enhancing the image, we developed a novel approach. The basic aim of edge enhancement is to modify the appearance of an image to make it visually more attractive or to improve the visibility of certain features specially the satellite images. The edge enhancement technique enhances all high spatial frequency detail in an image, including edges, lines and points of high gradients. In this approach, the details of edges in an image can be obtained by subtracting a smoothed image from the original [4]. This subtractive smoothing method has been used as the simplest way to obtain high spatial frequency image and this method of edge enhancement makes the image brighter and real edges are detected. In spite of all

these efforts, none of the proposed operators are fully satisfactory in real world applications. They do not lead to satisfactory results when used as a means of identifying locations at which to apply image sharpening. In this paper, the enhancement is applied through a framework of threshold decomposition. This has two advantages: it reduces the edge detection to a simple binary process; and it makes the estimation of edge direction straightforward. Edge detection and direction estimation may be carried out by identifying simple patterns, which are closely related to the Prewitt operators. Another method was proposed lately on satellite image enhancement which proposed an additional step by enhancing the brightness of the image before working on edge detection on Wavelet Sub bands. However, the process shows no statistical results in the research. Therefore, the quality evaluation was still un- identical in order to compare the results. In this study we proposed a novel approach satellite image sharpening on Discrete Curve let transform, we developed new algorithms in intensity evaluation and compare its quality with its original version on SVD and Morphological TOPAHT Transform. The processes were composed of image brightness, edge detection and the standard deviation of the image intensity performed by the Peak Signal to Noise Ratio (PSNR).

II. Satellite Image Enhancement

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human viewers and providing `better' input for other automated image processing techniques.

Fig 1 Satellite Image

The principal objective of image enhancement is to modify attributes of an image to make it more suitable for a given task and a specific observer. During this process, one or more attributes of the image are modified. The choice of attributes and the way they are modified are specific to a given task. Moreover, observer-specific factors, such as the human visual system and the observer's experience, will introduce a great deal of subjectivity into the choice of image enhancement methods. There exist many techniques that can enhance a digital image without spoiling it.

A.FDCT (Fast Discrete Curve Let Transform)

Actually the ridgelet transform is the core spirite of the curvelet transform. An anisotropic geometric wavelet transform, named ridgelet transform, was proposed by Candes and Donoho. The ridgelet transform is optimal at representing straight-line singularities. Unfortunately, global straight-line singularities are rarely observed in ral applications. To analyze local line or curve singularities, a natural idea is to consider a partition of the image, and then to apply the ridgelet transform to the obtained sub-images. Apart from the blocking effect, however, the application of this so-called first generation curvelet transform is limited because the geometry of ridgelets is itself unclear, as they are not ture ridge functions in digital images. The second-generation curvelet transform has been shown to be a very efficient tool for many different applications in image processing. The overview of the curvelet transform is shown below for four step:

Fig 2 Block Diagram of Curvelet Transform

There are some connection between Curvelet and Wavelet this part. The sub-band decomposition can be approximated using the well known wavelet transform:

 Using wavelet transform, f is decomposed into S0, D1, D2, D3, etc.

 P0 f is partially constructed from S0 and D1, and may include also D2 and D3.

 s f is constructed from D2s and D2s+1.

 P0 f is “smooth” (low-pass), and can be efficiently represented using wavelet base.

But it is confuse that the discontinuity curves effect the high-pass layers s f.

B. Singular Valued Decomposition (Svd)

SVD methods deal with solving difficult linear-least squares problems such as the terms in documents case and here colours in images. They are based on the following theorem of Linear Algebra. Each image can be represented by a matrix which contains the pixel intensity values. In general, for any image matrix A, the SVD can be defined as:

Where U and V are orthogonal square matrices and ΣA

matrix contains the sorted singular values on its main diagonal. ΣA contains the intensity information of the given image which means that the maximum singular value of ΣA contributes more than the other singular values.

C. Tophat Transform

The Filtering Mode buttons select whether to keep pixels that are Bright or dark compared to their surroundings. The Neighbourhood Radius slider adjusts the size of the interior region used in the top hat comparison (the outer or annular region is one or two pixels wide). The Threshold slider controls the difference between the brightest or darkest pixel value in the interior and surrounding regions that must be exceeded for the pixel to be retained, to produce the resulting Filtered Image shown on the right. It is actually the basic operation of binary morphology since almost all the other binary morphological operators can be derived from it.

Fig 3 Filtering process

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D. Morphology Multi Structuring Element

Morphology is a technique of image processing based on shapes. The value of each pixel in the output image is based on a comparison of the corresponding pixel in the input image with its neighbours. By choosing the size and shape of the neighbourhood, you can construct a morphological operation that is sensitive to specific shapes in the input image. In mathematical morphology, a structuring element is a shape, used to probe or interact with a given image, with the purpose of drawing conclusions on how this shape fits or misses the shapes in the image. It is typically used in morphological operations, such as dilation, erosion, opening, and closing.

E. Inverse Curvelet Transform

There is also procedural definition of the reconstruction algorithm. Basicly, inverse the procedure of curvelet transform with some mathematic revising:

Ridgelet Synthesis

Each „square‟ is reconstructed from the orthonormal ridgelet system. Summation all the Ridgelet coefficinets with basis:

Renormalization

Each ‟square‟ resulting in the previous stage is renormalized to its own proper square.

Smooth Integration

We reverse the windowing dissection to each of the windows reconstructed in the previous stage of the algorithm.

Subband Recomposition

We undo the bank of subband filters, using the reproducing formula to summation all the subbands:

III.Result Analysis

A. Discrete Wavelet Transform

Point: it affects only a limited number of coefficients. Hence the WT handles points discontinuities well. Curve:

Discontinuities across a simple curve affect all the wavelets coefficients on the curve.Hence the WT doesn‟t handle curves discontinuities well.

Fig 4 Image Enhancement using Discrete Wavelet Transform

B.Discrete Curvelet Transform

Curvelets are designed to handle curves using only a small number of coefficients. Hence the Curvelet handles curve discontinuities well.

Fig 5 Image Enhancement using Discrete Curvelet Transform

C. Quality Measurement

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image, if any of the pixels is set to 0, the output pixel is set to 0. After Morphological Process is used to sharpen these detected edges. Peak signal to noise ratio (PSNR) and root mean square error (RMSE) have been implemented in order to obtain quality results. PSNR can be obtained by using the following formula:

RMSE is representing input image I1 and proposed enhanced image I2 which can be obtained by the following formula:

Table 1 MSE values of FDCT and DWT

Fig 6 MSE values of FDCT and DWT

Table 2 PSNR values of FDCT and DWT

Fig 7 PSNR values of FDCT and DWT

IV. Conclusion

In Our Project We implemented on FDCT using SVD Process on Image TOPAHT Structuring FIlter This was done by accurately detecting the positions of the edges through on FDCT Subbands. The detected edges were then sharpened by applying smoothing and wrapping filter. By utilizing the multi-structure element edges, the scheme was capable to effectively sharpening and detecting fine details. The visual examples shown above, have demonstrated that the FDCT (Fast Discrete Curve let Transform) method was significantly better than many other well-known sharpener-type filters in respect of edge and fine detail restoration The PSNR improvement compared with DWT, FDCT technique is high.

References

[1] R. C. Gonzalez and R. E. Woods, Digital Image Processing. Englewood Cliffs, NJ: Prentice-Hall, 2007.s [2] A. R. Weeks, L. J. Sartor, and H. R. Myler, “Histogram specification of 24-bit color images in the color difference (C-Y) color space,” Proc. SPIE, vol. 3646, pp. 319–329, 1999.

[3] Li, S., Wang, Y., 2000. Multi sensor image fusion using discrete multi Wavelet Transform. In: Proc. 3rd Internat. Conf. on Visual Computing, pp. 93–103.

[4] E. J. Candies‟. L. Donohue. Curve lets:a surprisingly effective non-adaptive representation for objects with edges, “I curve and surface fitting : saint- malo 1999,A. cohen,C. Rabar, and L. L. Schumacher, Eds. Nashville, TN: Vanderbilt University Press, 1999.

[5] Chao Rue; Zhang Keg Li Yan-jun. An image fusion algorithm using Wavelet Transform [J]. Chinese Journal of Electronics,2004 32(5):750 753

[6] T. K. Kim, J. K. Paik, and B. S. Kang, “Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering,” IEEE Trans. Consum. Electron., vol. 44, no. 1, pp. 82–87, Feb. 1998.

[7] S. Chitwong, T. Boonmee, and F. Cheevasuvit, “Enhancement of color image obtained from PCA- FCM technique using local area histogram equalization,” Proc. SPIE, vol. 4787, pp. 98–106, 2002.

[8] H. Ibrahim and N. S. P. Kong, “Brightness preserving dynamic histogram equalization for image contrast enhancement,” IEEE Trans. Consum. Electron., vol. 53, no. 4, pp. 1752–1758, Nov. 2007.

[9] T. Kim and H. S. Yang, “A multidimensional histogram equalization by fitting an isotropic Gaussian mixture to a uniform distribution,” in Proc.IEEE Int. Conf. Image Process., Oct. 8–11, 2006, pp. 2865–2868.

[10] H. Demirel, G. Anbarjafari, and M. N. S. Jahromi, “Image equalization based on singular value decomposition,” in Proc. 23rd IEEE Int. Symp. Comput. Inf. Sci., Istanbul, Turkey, Oct. 2008, pp.1– 5.

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[12] D. Ghimire and J. Lee ,“ Nonlinear Transfer Function-Based Local Approach for Color Image Enhancement,” IEEE Transactions on Consumer Electronics, Vol. 57, No. 2, May 2011.

[13] R. K. Jha, R. Chouhan, P. K. Biswas ,“ Noise-induced Contrast Enhancement of Dark Images using Non-dynamic Stochastic Resonance,” 978-1-4673-0816-8/12S ©2012 IEEE.

[14] Z. Chaofu, M. Li-ni, J. Lu-na ,“ Mixed Frequency domain and spatial of enhancement algorithm for infrared image”, 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012)

[15] J. L. Stark, F. Muztagh, and A. Bijaoui, Image Processing and Data Analysis: The Multi scale Approach. Cambridge, U.K.: Cambridge Univ. Press, 1998.

[16] Donohue, D. L., Minimum Entropy Segmentation, in Wavelets: Theory, Algorithms and Applications, C. K. Chui, L. Montefusco and L. Pucker (eds.), Academic Press, San Diego, 1994, 233{270}.

Figure

Fig 1 Satellite Image
Fig 4 Image Enhancement using Discrete Wavelet Transform
Fig 7 PSNR values of FDCT and DWT

References

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