Texture Synthesis Based On Image Resolution Enhancement Using Wavelet Transforms by ijcsiseditor


									                                                              (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                      Vol. 10, No. 4, 2012

            Texture Synthesis based on image resolution
              enhancement using wavelet transforms
     G. Venkata Rami Reddy                                     S.Kezia                                       Dr.V.Vijaya Kumar
  Associate professor , CSE Dept.                         Associate Prof.                             Professor and Dean of CSE,IT &
 School of Information Technology                            ECE Dept.                                MCA Depts., Godavari Institute of
    JNT University Hyderabad                            CIET, Rajahmundry                               Engg. & Tech.,Rajahmundry,
         Hyderabad,India                                      AP, India                                           AP, India
     gvr_reddi@yahoo.co.in                           sakakezia1981@gmail.com                               vijayvakula@yahoo.com

Abstract— In this paper, we propose a Wavelet and Stationary              image and create a synthesized image by minimizing the
domain normalization (WSDN) technique for texture synthesis.              overlap error in overlapping regions. Tiling-based methods
The proposed WSDN improve the image resolution by estimating              precompute a set of small tiles with boundary pixels colored in
the high frequency band information. The proposed technique is            such a way that no seam is apparent between abutting tiles.
based on the idea of splitting the texture synthesis problem into
three stages. In the first stage stationary and discrete wavelet              Resolution enhancement of pictorial data is desirable in
transforms are applied on the original low resolution image. The          many applications such as monitoring, surveillance, medical
LH, HL, HH subbands generated after applying DWT is                       imaging and remote sensing.             It is a classic signal
interpolated. In the second stage, estimated LH, HL, HH                   interpolation problem and conventional approaches such as
subbands are generated by the normalization technique. In the             zero-order interpolation (sample-and-hold) cause severe
third stage inverse DWT (IDWT) is applied to generate                     pixelation impairments while bilinear and spline interpolation
synthesized image. To test the efficacy of the proposed method            invariably result in undesirable levels of smoothing across
PSNR values are calculated and compared with the existing                 salient edges. Recently several efforts in the field have utilized
methods. The experimental results clearly indicate the efficacy of        wavelet-domain methodologies with the intention of
the proposed method over the existing method.                             overcoming some of the problems associated with
                                                                          conventional treatment. A common feature of these algorithms
                                                                          is the assumption that the low resolution (LR) image to be
   Keywords-Wavelet Transform; Interpolation; image resolution            enhanced is the lowpass filtered subband of a high resolution
                                                                          (HR) image which has been subjected to a decimated wavelet
                                                                          transform. A trivial approach would be to reconstruct an
                       I.    INTRODUCTION                                 approximation to the HR image by filling the unknown, so
    Texture synthesis has many applications in image                      called ‘detail’ subbands (normally containing highpass spatial
processing, computer vision and graphics [1]. It can be                   frequency information) with zeros followed by the application
described as follows: given a sample texture image, a new                 of the inverse wavelet transform (IWT). It is interesting to
texture image is synthesized, which should be sufficiently                note that while this approach is capable of outperforming
different from the original one, yet appears perceptually to be           bilinear interpolation it has never appeared in the literature
generated by the same underlying stochastic process. There                probably due to its simplicity. More sophisticated methods
are two essential criteria in evaluating a texture synthesis              have attempted to estimate the unknown detail wavelet
algorithm: quality and speed.                                             coefficients in an effort to improve the sharpness of the
                                                                          reconstructed images.
    Example based texture synthesis uses a given example                      Image-resolution enhancement in the wavelet domain is a
image to create large images with similar visual                          relatively new research topic, and, recently, many new
characteristics. It is used in video games, flight simulators and         algorithms have been proposed [2], [3]. Complex wavelet
scientific computations which require rapid high-resolution               transform (CWT) [4] is one of the recent wavelet transforms
texturing of surfaces and at a less cost in texture memory in             used in image processing. A one level CWT of an image
the graphics processors (GPUs). There are a number of                     produces two complex valued low frequency subband images
algorithms for example-based texture synthesis. In general,               and six complex valued high-frequency subband images. The
they can be divided into three categories: pixel-based methods,           high frequency subband images are the result of direction
patch-based methods and tiling-based methods. Pixel-based                 selective filters. They show peak magnitude responses in the
methods use neighborhood information for each pixel in the                presence of image features oriented at +75◦, +45◦, +15◦, −15◦,
example image to identify the most likely value for                       −45◦, and −75◦ [5].In [6] a dual-tree CWT (DT-CWT) is used
neighboring pixels during synthesis. Patch-based methods                  to decompose a low resolution image into different subband
look iteratively for optimized sub-images in the example

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                                                                                                      ISSN 1947-5500
                                                              (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                      Vol. 10, No. 4, 2012
images. Then the six complex valued high frequency subband                existence of multiple LR images. Finally, a similar approach
images are interpolated using bicubic interpolation. In parallel,         was proposed in [18] on the basis of the availability of a single
the input image is also interpolated separately. Finally, the             LR image. The basis of this approach, MBFR technique, was
interpolated high frequency subband images and interpolated               designed to take advantage of the non-uniform sampling of a
input image are combined by using inverse DT-CWT (IDT-                    signal using sections with higher sampling rates to interpolate
CWT) to achieve a high resolution output image. In [7] and                higher frequencies locally. However availability of only a
[8] estimation was carried out by examining the evolution of              single LR image, with implication that the sampling is
wavelet transform extrema from finer to coarser subbands.                 uniform, prohibits taking full advantage of this scheme.
Edges identified by an edge detection algorithm in lower                  Recently it has been shown that the cycle-spinning
frequency subbands were used to formulate a template for                  methodology produces notable results when adapted to
estimating edges in higher frequency subbands. Only                       wavelet domain resolution enhancement problems [19].
coefficients with significant magnitudes were estimated as the
evolution of the wavelet coefficients among the scales was                    In this work, an image resolution enhancement technique
found to be difficult to model for other coefficients.                    which generates sharper high resolution image is proposed.
Significant magnitude coefficients correspond to salient image            The proposed technique uses DWT to decompose a low
discontinuities and consequently only the portrayal of those              resolution image into different subbands. Then the three high
can be targeted with this approach while moderate activity                frequency subband images have been interpolated using
detail escapes treatment. Furthermore, due to the fact that               bicubic interpolation. The high frequency subbands obtained
wavelet filters have support which spans a number of                      by Stationary Wavelet Transform (SWT) of the input image
neighbouring coefficients, edge reconstruction is inevitably              are being incremented into the interpolated high frequency
based on contributions from such neighbourhoods. As                       subbands and normalized to the number of pixels in the
methods based on extrema evolution only target locations of               original low resolution image in order to correct the estimated
coefficients with significant magnitudes, such neighbourhoods             coefficients. In parallel, the input image is also interpolated
will inevitably provide incomplete information ultimately                 separately. Finally, corrected interpolated high frequency
affecting the quality of edge reconstruction. Performance is              subbands and interpolated input image are combined by using
also affected by the fact that the signs of estimated coefficients        inverse DWT (IDWT) to achieve a high resolution output
are replicated directly from ‘parent’ coefficients (in a quadtree         image.
hierarchical decomposition sense) without any attempt being                   The paper is organized as follows: section II deals with
made to estimate the actual signs. This is contradictory to the           wavelet transforms, section III deals with methodology,
commonly accepted fact that there is very low correlation                 section IV deals with results and discussions and section V
between the signs of parent coefficients and their descendants.           deals with conclusions.
In a coding context for example, the signs of descendants were
generally assumed to be random [9], [10]. As a result, the                                   II. WAVELET TRANSFORM
signs of the coefficients estimated using extrema evolution
techniques cannot be relied upon.                                             The DWT (Discrete Wavelet Transform) transforms
                                                                          discrete signal from time domain into time- frequency domain.
    In [11] a technique was proposed which takes into account             The transformation product is set of coefficients organized in
the Hidden Markov Tree (HMT) approach of [12]. The latter                 the way that enables not only spectrum analyses of the signal,
was successfully applied to a different class of problems                 but also spectral behavior of the signal in time. Wavelets have
including image denoising and related applications. An                    the property of smoothness [20]. Such properties are available
extended version of this approach utilizing super resolution              in both orthogonal and Biorthogonal wavelets. However, there
type of methodologies is presented in [13]. These methods                 are special properties that are not available in the orthogonal
model the unknown wavelet coefficients as belonging to                    wavelets, but exist in Biorthogonal wavelets, that are the
mixed Gaussian distributions (states) which are symmetrical               property of exact reconstruction and symmetry. Another
around the zero mean. HMT models are used to find out the                 advantageous property of Biorthogonal over orthogonal
most probable state for the coefficient to be estimated (i.e. to          wavelets is that they have higher embedding capacity if they
which distribution it belongs to). The posterior state is found           are used to decompose the image into different channels. All
using state transition information from lower resolution scales           these properties make Biorthogonal wavelets promising in the
and the coefficient estimates are randomly generated using this           resolution enhancement domain [21].
distribution. Being symmetrical around zero, the probability of
estimation of a coefficient with a negative sign is equal to that
with a positive sign. Consequently sign changes between the
scales are not taken into account and randomly generated signs                                 III.   METHODOLOGY
are assigned to the estimated coefficients. Finally the HMT               The proposed algorithm consists of six steps. In the first step,
based method has been further developed so that it does not               discrete and stationary wavelet transforms (with Daubechies
require any training data set [14].                                       9/7 as the wavelet function) are applied on the low resolution
   In [15] and [16] a wavelet based super resolution method               input image. Three high frequency subbands are (LH, HL, and
was presented based on the Multiresolutional Basis Fitting                HH) obtained after applying DWT, which contain the high
Reconstruction (MBFR) technique in [17]. The algorithm                    frequency components of the input image. In step two bicubic
exploits the interlaced sampling structure in the LR data in the          interpolations with enlargement factor of 2 is applied to high

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                                                                                                      ISSN 1947-5500
                                                                (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                        Vol. 10, No. 4, 2012
frequency sub band images of the first step. In the third step                              IV.    RESULTS AND DISCUSSION
SWT is employed on the low resolution image to minimize the
information loss. In the fourth step, the interpolated high
frequency subbands and the SWT high frequency subbands are
normalized to the total number of pixels in the original low
resolution image. The normalization is carried out by adding
SWT and DWT sub bands and dividing them by a factor of m
x n. m and n are the dimensions of the original low resolution
image. To increase the resolution of the image the input image
and high frequency image of the fourth step are interpolated in                     (a)                         (b)                     (c)
step five. In step six the IDWT is applied on the interpolated
images of the step five to obtain the high resolution                    Figure 2. Results for Food0 (a) Original low resolution texture image (b)
                                                                         Existing method (c) Proposed method.
synthesized image. The flowchart for the proposed algorithm
is shown in Fig.1.


                                                                                    (a)                         (b)                     (c)
                 SWT                   DWT
                                                                         Figure 3. Results for Water0 (a) Original low resolution texture image (b)
                                                                         Existing method (c) Proposed method.
    L     L     H       H       L      L     H      H
    L     H     L       H       L      H     L      H

                                                    tion with
                                                     factor 2

              Normali        Normali         Normali
              zation          zation          zation
                                                                                    (a)                         (b)                     (c)
                                                                         Figure 4. Results for Bark5 (a) Original low resolution texture image (b)
                                                                         Existing method (c) Proposed method.
                                                                             The proposed technique is tested on Vistex textures.
              Estima         Estim         Estim                         Fig.2a, 3a and 4a show the original images. Fig 2b, 3b and 4b
              ted LH          ated          ated                         are the outputs of the existing method [22]. Fig 2c, 3c and 4c
                              HL            HH                           are the synthesized images of the proposed method.
                                                                             The original high resolution images are used as the ground
                        IDWT                                             truth and the enhancement results are evaluated with respect to
                                      Interpolation                      the peak signal-to-noise ratio (PSNR). The outputs of the
    Interpolation                    with factor α/2                     proposed method are compared with the existing methods
   with factor α/2                                                       given in [22,23,24,25,26,27,28,29,30] .The textures of size
                                                                         256x256 are taken as input images and the size of the
                          High                                           synthesized output image is 512x512.
                         image                                               Table I show the PSNR results of the proposed technique
                        (αmxαn)                                          for VisTex textures. Table II compares the PSNR performance
                                                                         of the proposed technique with the existing method [22]. Table
                                                                         III shows the comparison of different techniques with the
Figure 1. Block Diagram of the proposed algorithm                        proposed technique. Table III clearly show that the PSNR
                                                                         value of the proposed method is high when compared to the all
                                                                         other methods.

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                                                                                                          ISSN 1947-5500
                                                                        (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                                Vol. 10, No. 4, 2012
  TABLE I.            PSNR RESULTS FOR RESOLUTION ENHANCEMENT FROM                                            ACKNOWLEDGMENT
                        256X256 TO 512X512 OF THE PROPOSED METHOD
                                                                                   I would like to thank Prof. Rameswara Rao, Vice Chancellor
          Texture                     PSNR (dB) of                                 for encouraging research Programmes. The authors would like
                                     Proposed method                               to express their gratitude to Sri K.V.V. Satyanarayana Raju,
                                                                                   Chairman, and Sri K. Sasi Kiran Varma, Managing Director,
             Food0                        31.29
                                                                                   Chaitanya group of Institutions for providing necessary
             Water0                       29.95                                    Infrastructure. Authors would like to thank the anonymous
             Water1                       34.40                                    reviewers for their valuable comments.
             Bark5                        47.70
             Brick0                       37.61
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          Fabric4                         30.53
                                                                                          for Analysis and Synthesis”, IEEE TRANSACTIONS ON IMAGE
          Leaves1                         50.90                                           PROCESSING, VOL. 7, NO. 10, OCTOBER 1998.
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          Leaves0                         43.19                                           inter-subband correlation in wavelet domain,” in Proc. ICIP, 2007, vol.
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                        FROM 256X256 TO 512X512                                           image interpolation,” IEEE Trans. Image Process., vol. 8, no. 9, pp.
                                                                                          1295–1297, Sep. 1999.
 Technique            Food 0         Water 0           Bark 5                      [4]    N. G. Kingsbury, “Image processing with complex wavelets,”
  Proposed            31.29dB        29.95dB           47.70dB                            Philos.Trans. R. Soc. London A, Math. Phys. Sci., vol. 357, no. 1760, pp.
                                                                                          2543–2560, Sep. 1999.
  Existing            30.67dB        29.33dB           47.49 dB                    [5]    T. H. Reeves and N. G. Kingsbury, “Prediction of coefficients from
                                                                                          coarse to fine scales in the complex wavelet transform,” in Proc. IEEE
                                                                                          ICASSP, Jun. 5–9, 2000, vol. 1, pp. 508–511.
     128X128 TO 512X512 OF THE PROPOSED TECHNIQUE COMPARED WITH                    [6]    Hasan Demirel and Gholamreza Anbarjafari ,”Satellite Image Resolution
     THE CONVENTIONAL AND STATE-OF-ART IMAGE RESOLUTION                                   Enhancement Using Complex Wavelet Transform”, IEEE
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                                                                                          1297, Sep. 1999.
     WZP(db.9/7)                 28.84         30.44        21.47    29.57         [9]    J.M. Shapiro, Embedded Image Codi Wavelet Coefficients, IEEE Trans.
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 Regularity- preserving
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Image Interpolation [23]
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      HMM [25]                   28.86         30.46        21.47    29.58         [11]   K. Kinebuchi, D.D. Muresan and T.W. Parks, “Imalation Using
    HMM SR [26]                  28.88         30.51        21.49    29.60                Wavelet-Based Hidden Markov Trees”, Proc. ICASSP ‘01, vol. 3, pp. 7-
                                                                                          11, May 2001.
     WZP-CS [27]                 29.27         30.78        21.54    29.87
                                                                                   [12]   M.S. Crouse, R.D. Nowak and R.G. Baraniuk,” Wavelet-Based
  WZP-CS-ER [28]                 29.36         30.89        21.56    30.05                Statistical Signal Processing Using Hidden Markov Models”, IEEE
                                                                                          Trans. Signal Proc., vol.46,no.4, pp.886–902, Apr. 1998.
    DWT SR [29]                  34.79         32.73        23.29    32.19
    CWT SR [30]                  33.74         33.05        23.12    31.03         [13]   S. Zhao, H. Han and S. Peng, “Wavelet Domain HMT-Based Image
       SWT SR                    32.01         31.25        22.74    29.46                Superresolution”, IEEE International Conference on Image Proc., vol.
                                                                                          2, pp. 933-936, Sep. 2003.
 Existing Method [22]            34.82         35.01        23.87    33.06
  Proposed method                34.97         35.22        30.90    33.43         [14]   D.H. Woo, I.K. Eom and Y.S. Kim, “Image Interpolation based on inter-
                                                                                          scale dependency in wavelet domain”, Proc. ICIP ‘04., Oct. 2004.
                                V.   CONCLUSION                                    [15]   N. Nguyen, “Numerical Techniques for Image Superresolution ”, Ph.D.
                                                                                          dissert., Stanford Uni., Stanford, CA, Apr. 2000 .
    The proposed WSDN technique uses DWT to decompose                              [16]   N. Nguyen, P. Milanfar, “An efficient wavelet-based algorithm for
an image into different subband images, and then the high-                                image superresolution”,Proc. ICIP ‘00, vol.2, pp. 351-354, Sep. 2000.
frequency subband images are interpolated. The interpolated                        [17]   C.Ford and D.M.Etter , “Wavelet Basis Reconstruction of
high frequency subband coefficients have been corrected by                                Nonuniformly Sampled Data”, IEEE Trans. Circ. & Syst., vol.45, no.8,
using the high frequency subbands achieved by SWT of the                                  pp.1165–1168, Aug. 1998.
input image. The PSNR values of table I and II shows the                           [18]   S. Mitevski and M. Bogdanov, “Application of Multiresolutional Basis
efficacy of the proposed WSDN method over the other                                       Fitting Reconstruction in Image Magnifying”, Proc. 9th
                                                                                          Telecomnications Forum, pp. 565-568, Nov. 2001.
                                                                                   [19]   A. Temizel and T. Vlachos, “Wavelet Domain Image Resolution
                                                                                          Enhancement Using Cycle-Spinning”, IEE Electronics Letters, vol. 41,
                                                                                          no. 3, Feb. 2005.

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                                                                                                                      ISSN 1947-5500
                                                                          (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                                  Vol. 10, No. 4, 2012
[20] Burrus, C. S., R. A., Gopinath, and H., Guo,. “Introduction to Wavelets                                          S.Kezia received the B.Tech(ECE) degree from
     and Wavelet Transforms: A Primer”, Prentice-Hall, Inc. 1998.                                                    JNTU College of Engineering, Kakinada, JNT
[21] Daubechies, I., 1994. “Ten lectures on wavelets”, CBMS, SIAM, pp                                                University in 2002. She received M.Tech from
     271-280.                                                                                                        IIT Madras, India in 2004. She is having nearly 7
                                                                                                                     years of teaching and industrial experience. She
[22] Hasan Demirel and Gholamreza Anbarjafari ,“Image Resolution
     Enhancement by Using Discrete and Stationary Wavelet                                                            is currently working as Associate Professor, Dept
     Decomposition”, IEEE Transactions on Image Processing, Vol. 20, No.                                             of E.C.E,Chaitanya Institute of Engineering and
     5, May 2011.                                                                                                    Technology, Rajahmundry, Andhrapradesh,
                                                                                                                     India. She is pursuing her Ph.D from JNT
[23] W. K. Carey, D. B. Chuang, and S. S. Hemami, “Regularity-preserving               University, Kakinada in ECE under the guidance of Dr. V. Vijaya Kumar and
     image interpolation,” IEEE Trans. Image Process., vol. 8, no. 9,                  Dr.I.Santi Prabha. She is a life member of ISTE, Red cross Society and she is
     pp.1295–1297, Sep. 1999.                                                          a member of SRRF-GIET, Rajahmundry. She has presented 2 papers in
[24] X. Li and M. T. Orchard, “New edge-directed interpolation,” IEEE                  International Journals and 4 papers in various National, Inter National
     Trans. Image Process., vol. 10, no. 10, pp. 1521–1527, Oct. 2001.                 conferences proceedings.
[25] K. Kinebuchi, D. D. Muresan, and R. G. Baraniuk, “Waveletbased
                                                                                                                   Vakulabharanam Vijaya Kumar received
     statistical signal processing using hidden Markov models,”                                                    integrated M.S. Engg, degree from Tashkent
     in Proc. Int. Conf. Acoust., Speech, Signal Process., 2001, vol.                                              Polytechnic Institute, Associate Professor and
     3, pp. 7–11.                                                                                                  taught courses for M.Tech students. He has been
[26] S. Zhao, H. Han, and S. Peng, “Wavelet domain HMT-based image                                                 working as Dean Computer sciences and Head
     super resolution,” in Proc. IEEE Int. Conf. Image Process., Sep. 2003,                                        Srinivasa Ramanujan Research Forum-GIET,
                                                                                                                   Rajahmundry, Affiliated to JNT University,
     vol. 2, pp. 933–936.                                                                                          Kakinada. His research interests include Image
[27] A. Temizel and T. Vlachos, “Wavelet domain image resolution                                                   Processing, Pattern Recognition, Network
     enhancement using cycle-spinning,” Electron. Lett., vol. 41, no. 3, pp.                                       Security, Steganography, Digital Watermarking,
     119–121, Feb. 3, 2005.                                                            and Image retrieval. He is a life member for CSI, ISC, ISTE, IE (I), IRS, ACS,
[28] A. Temizel and T. Vlachos, “Image resolution upscaling in the wavelet             CS and Red Cross. He has published more than 100 research publications in
     domain using directional cycle spinning,” J. Electron. Imag., vol. 14, no.        various National, Inter National conferences, proceedings and Journals.
     4, 2005.
[29] G. Anbarjafari and H. Demirel, “Image super resolution based on
     interpolation of wavelet domain high frequency subbands and the
     spatial domain input image,” ETRI J., vol. 32, no. 3, pp. 390–394,
     Jun. 2010.
[30] H. Demirel and G. Anbarjafari, “Satellite image resolution enhancement
     using complex wavelet transform,” IEEE Geoscience and Remote
     Sensing Letter, vol. 7, no. 1, pp. 123–126, Jan. 2010.

                              AUTHORS PROFILE

                        G.Venkata rami reddy received the M.Tech.
                        (CSE) degree from JNT University Hyderabad in
                        1998. He is working in JNT University since 2000.
                        Presently he is working as an Associate Professor in
                        Dept of CSE in School of Information Technology,
                        JNT University Hyderabad. He is more than 11
                        years of experience in teaching and Software
                        Development. . He is pursuing his Ph.D. in the area
                        of Image processing from JNT University Hyderabd
in Computer Science and Engineering under the guidance of Dr. M. Anji
Reddy. He is presented more than 6 National and International journal and
conference. His areas of interests are image processing, computer networks,
analysis of algorithms.

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