Avalable Onlne at www.csmc.com Internatonal Journal of Computer Scence and Moble Computng A Monthly Journal of Computer Scence and Informaton Technology IJCSMC, Vol. 3, Issue. 4, Aprl 2014, pg.1315 1320 RESEARCH ARTICLE ISSN 2320 088X RESOLUTION ENHANCEMENT OF SATELLITE IMAGES USING DUAL-TREE COMPLEX WAVELET AND CURVELET TRANSFORM P.SUBBULAKSHMI PG Scholar Sr Shakth Insttute of Engneerng and Technology Combatore. V.SHALINI PG Scholar Sr Shakth Insttute of Engneerng and Technology Combatore. Abstract Resoluton enhancement(re) methods that are ndependent of wavelets.e. nterpolaton methods leads to blurrng as hgh frequency components are lost.re scheme based on Dscrete wavelet transform(dwt) leads to artfacts due to shft varant property. A complex wavelet-doman mage resoluton enhancement algorthm based on dual-tree complex wavelet transform (DT-CWT) wth non local means(nlm) and curvelet transform s proposed. In ths scheme, the low resoluton mage s undergone curvelet transform for denosng. The hgh frequency sub bands obtaned by DT-CWT of the resultant denosed mage are nterpolated usng Lanczos nterpolator. The hgh frequency sub bands are further passed through an NLM flter to cater for the artfacts generated by DT-CWT.The low resoluton nput mage and the fltered hgh frequency sub bands are combned usng nverse DT-CWT to obtan a resoluton-enhanced mage. The quanttatve peak sgnal-to-nose rato (PSNR) and results are presented to reveal the superorty of the proposed technque through comparsons between state-of-the-art resoluton enhancement methods. Keywords- Resoluton enhancement, mage nterpolaton,shft varant, dual-tree complex wavelet transform,curvelet transform, Lanczos nterpolaton, dscrete wavelet transform, satellte mage I. INTRODUCTION Resoluton s an mportant consderaton n all mage and vdeo processng applcatons lke feature extracton, satellte mage resoluton enhancement and vdeo resoluton enhancement. Satellte mages are used n many applcatons lke astronomy, geoscentfc studes and geographcal nformaton systems. Resoluton enhancement of mages s a preprocess that has to be used for many satellte mage processng applcatons such as vehcle recognton, brdge recognton, and buldng recognton. Image resoluton enhancement methods can be categorzed nto two maor classes namely 1) Spatal doman; and 2) Frequency-doman The term spatal doman refers to the mage plane tself and approaches n ths category are based on drect manpulaton of pxels n an mage. However frequency doman processng technques use many transformatons such as to acheve a hgh resoluton mage. 2014, IJCSMC All Rghts Reserved 1315
Interpolaton has been used commonly for resoluton enhancement n mage processng wdely[2],[3]. There are four well known nterpolaton technques, namely nearest neghbour nterpolaton, blnear nterpolaton, b-cubc nterpolaton and Lanczos nterpolaton. A smple method of multvarate nterpolaton n one or more dmensons s nearest-neghbor nterpolaton. The nearest neghbor method selects the value of the nearest pont and does not consder the values of neghborng ponts at all, yeldng a pecewse-constant nterpolant. Ths method results n edge dstorton. Blnear nterpolaton consders the closest 2x2 neghborhood of known pxel values surroundng the unknown pxel's computed locaton. It then takes a weghted average of these 4 pxels to arrve at ts fnal, nterpolated value. Blnear nterpolaton however produce a greater number of nterpolaton artfacts such as alasng, blurrng, and edge halos. B-cubc nterpolaton consders the 16 pxels around t (4x4 pxels) whle computng an average. Images re-sampled wth bcubc nterpolaton become smoother and have fewer nterpolaton artfacts compared to blnear nterpolaton. The Lanczos nterpolaton whch s a wndowed form of snc flter s better than the other nterpolaton methods because t has the ncreased ablty to detect edges and lnear features. It offers good results by showng reducton n blurrng, alasng etc [4]. Resoluton enhancement schemes that are not based on wavelets suffer from the drawback of losng hgh-frequency contents leadng n blurred mage. But Wavelet transform retan these hgh frequency components because these transforms provde tme and frequency representaton smultaneously. Hence resoluton enhancement usng wavelet transforms s most preferable. Hence wavelet transforms lke Dscrete Transform(DWT) and Statonary Wavelet Transform(SWT) were used for resoluton enhancement. A dscrete wavelet transform (DWT) s any wavelet transform for whch the wavelets are dscretely sampled. The two dmensonal DWT mplementaton decomposes mage nto three detaled sub-mages (LH, HL and HH) correspondng to three dfferent drectonal orentatons (vertcal, horzontal and dagonal) and lower resoluton sub-mage LL. The sub-mage LL s now a parent mage and further s decomposed nto four chld mages for multlevel wavelet analyss. In DWT-based resoluton scheme, a common assumpton that the low-resoluton (LR) mage s the low-pass fltered subband of the wavelet-transformed hghresoluton (HR) mage. Ths requres that wavelets coeffcents n subbands should be estmated wth hgh-pass spatal frequency nformaton n order to estmate the HR mage from the LRmage. These DWT-based resoluton enhancement schemes n [5] generate artfacts (due to DWT shft-varant property). The Statonary wavelet transform (SWT) s desgned to overcome the lack of translaton-nvarance of the dscrete wavelet transform (DWT). Translaton-nvarance s acheved by removng the downsamplers and up-samplers n the DWT and up-samplng the flter coeffcents by a factor of 2( 1) n the th level of the algorthm[6]. The conventonal DWT s very senstve to shfts n the nput sgnal; t has poor drectonal selectvty and also lacks the phase nformaton due to real valued coeffcents. To overcome the lmtatons by these conventonal resoluton enhancement schemes a new complex wavelet doman resoluton algorthm(dt_cwt) has been proposed. DT-CWT s shft nvarant and tends to have mproved drectonal resoluton compared to DWT. It also has lmted redundancy. These propertes make DT-CWT coeffcents nherently nterpolable. Hence these features make DT-CWT to be a better approach for mage resoluton enhancement. Here a DT-CWT and curvelet based nonlocal-means RE technque s proposed usng the DT-CWT, curvelet transform, Lanczos nterpolaton, and NLM. II. INTRODUCTION TO DUAL-TREE COMPLEX WAVELET TRANSFORM,CURVELET TRANSFORM & NON LOCAL MEANS The complex wavelet transform (CWT) s a complex-valued extenson to the standard dscrete wavelet transform (DWT). It s a two-dmensonal wavelet transform whch provdes mult resoluton, sparse representaton, and useful characterzaton of the structure of an mage. Further, t purveys a hgh degree of shft-nvarance n ts magntude. However, a drawback to ths transform s that t s exhbts (where d s the dmenson of the sgnal beng transformed) redundancy compared to a separable DWT. These CWTs employ two conventonal DWT flter bank trees workng n parallel such that respectve flters of both the trees are n approxmate quadrature. As t s shft nvarant t has mproved drectonalty and leads to perfect drectonal resoluton and ads perfect reconstructon of the satellte mage. The Dual-tree complex wavelet transform (DTCWT) calculates the complex transform of a sgnal usng two separate DWT decompostons (tree a and tree b). If the flters used n one are specfcally desgned dfferent from those n the other t s possble for one DWT to produce the real coeffcents and the other the magnary. Ths redundancy of two provdes extra nformaton for analyss but at the expense of extra computatonal power. It also provdes approxmate shft-nvarance (unlke the DWT) yet stll allows perfect reconstructon of the sgnal. The desgn of the flters s partcularly mportant for the transform to occur correctly and the necessary characterstcs are: The low-pass flters n the two trees must dffer by half a sample perod Reconstructon flters are the reverse of analyss All flters from the same orthonormal set 2014, IJCSMC All Rghts Reserved 1316
Tree a flters are the reverse of tree b flters Both trees have the same frequency response Fgure 1 Block dagram of 3-level DT-CWT Most natural mages/sgnals exhbt lne-lke edges,.e., dscontnutes across curves (so-called lne or curve sngulartes). Although applcatons of wavelets have become ncreasngly popular n scentfc and engneerng felds, tradtonal wavelets perform well only at representng pont sngulartes, snce they gnore the geometrc propertes of structures and do not explot the regularty of edges. Therefore, wavelet-based compresson, denosng, or structure extracton become computatonally neffcent for geometrc features wth lne and surface sngulartes. Curvelets are a non-adaptve technque for mult-scale obect representaton. Curvelets are an approprate bass for representng mages (or other functons) whch are smooth apart from sngulartes along smooth curves, where the curves have bounded curvature,.e. where obects n the mage have a mnmum length scale. A curvelet transform dffers from other drectonal wavelet transforms n that the degree of localsaton n orentaton vares wth scale. In partcular, fne-scale bass functons are long rdges; the shape of the basc functons at scale s orentaton. 2 2 by 2 so the fne-scale bases are sknny rdges wth a precsely determned The NLM flter (an extenson of neghborhood flterng algorthms) s based on the assumpton that mage content s lkely to repeat tself wthn some neghborhood (n the mage) and n neghborng frames. Non-local means flter s an algorthm used partcularly for mage denosng. Unlke other local smoothng flters, non-local means flter averages all observed pxels to recover a sngle pxel. The weght of each pxel depends on the dstance between ts ntensty grey level vector and that of the target pxel. III. PROPOSED SYSTEM A complex wavelet-doman approach based on dual-tree complex wavelet transform (DT-CWT) wth non local means(nlm) and curvelet transform for mage resoluton enhancement s proposed. Fgure 2 Block dagram of the proposed algorthm 2014, IJCSMC All Rghts Reserved 1317
In the proposed algorthm the LR nput mage s undergone curvelet denosng. The resultant nput mage s decomposed nto dfferent subbands (.e., C and W, where {A,B,C,D} and {1, 2, 3}) by usng DT-CWT. C values are the mage coeffcent subbands, and W are the wavelet coeffcent subbands. DT-CWT decomposton s done n three levels. The subscrpts A, B, C, and D represent the coeffcents at the even-row and even-column ndex, the odd-row and even-column ndex, the even-row and odd-column ndex and the odd-row and odd-column ndex, respectvely. W values are nterpolated usng the Lanczos nterpolaton (havng good approxmaton capabltes) and combned wth the LR nput mage. Snce C contans low-pass-fltered mage of the LR nput mage, therefore, hgh-frequency nformaton s lost. To cater for t, the LR nput mage s used nstead of C.Although DT-CWT s shft nvarant t may ntroduce artfacts after the nterpolaton of W.Therefore NLM flterng s used to compensate for these artfacts,. All nterpolated W values are passed through the NLM flter. Then the nverse DT-CWT s appled to these fltered subbands along wth the nterpolated LR nput mage to reconstruct the Hgh resoluton(hr) mage. The results reveal that the proposed algorthm performs better than the exstng wavelet-doman RE algorthms n terms of the peak-sgnal-to-nose rato (PSNR).The output mage wll have sharper edges than the nterpolated mage obtaned by nterpolaton of the nput mage drectly. The PSNR calculates the peak sgnal-to-nose rato, n decbels, between two mages. Ths rato s used as a qualty measurement between the orgnal and a reconstructed mage. The hgher the PSNR, the better the qualty of the reconstructed mage. The MSE(Mean Squared Error) represents the cumulatve squared error between the reconstructed and the orgnal mage, whereas PSNR represents a measure of the peak error. The lower the value of MSE, the lower the error. To compute PSNR the mean-squared error s frst calculated usng the followng equaton: MSE I 2 1( m, n) I2( m, n) M, N M * N where M and N are the number of rows and columns n the nput mage. PSNR can be calculated as follows 2 R PSNR 10 log 10 MSE where R s the maxmum fluctuaton n nput mage. For a 8-bt mage value of R s 255.The Mean Square Error (MSE) and the Peak Sgnal to Nose Rato (PSNR) are the two error metrcs used to compare mage reconstructon qualty. IV. RESULTS AND DISCUSSION Ths secton presents the results obtaned for the proposed resoluton enhancement scheme. Inorder to reveal the effectveness of the proposed scheme over the conventonal and state-of-the-art mage resoluton enhancement technques, dfferent LR optcal mages obtaned from the Satellte Imagng Corporaton webpage [1] were tested. The mage of Washngton DC ADS40 Orthorectfed Dgtal Aeral Photography s taken for comparson wth exstng RE technques such as SWT,DWT,DT-CWT and DT-CWT_NLM schemes.fgure 3 shows the orgnal Washngton DC mage, the down sampled nput mage, and the mages obtaned usng SWT-RE, DWT-RE, DT-CWT-RE,DWT-CWT-NLM-RE and the proposed RE schemes. The low resoluton nput mage when subected to SWT splts the mage nto dfferent sub-bands and these sub-bands wll have the same sze as that of nput mage. The HR mage reconstructed from these sub-bands s shown n Fgure 3(c).When the LR mage s subected to DWT decomposes the mage nto many subbands. The hgh frequency sub-bands and nput LR mage are nterpolated and combned usng nverse DWT to get the HR mage as shown n Fgure 3(d). 2014, IJCSMC All Rghts Reserved 1318
The HR mage reconstructed usng DT-CWT s shown n Fgure 3(e). s shown n Fgure 3(f).The HR mage reconstructed usng DT-CWT contans few artfacts and hence hgh frequency subbands are passed through NLM flter to cater for these artfacts. The HR mage obtaned from ths DT-CWT-NLM RE Scheme s shown n Fgure 3(f). (g) Fgure 3 (a) Orgnal Washngton DC mage (b) Input mage (c) SWT- RE(d)DWT-RE (e) DT-CWT-RE (f)dt-cwt-nlm-re (g) Proposed RE scheme The HR mage obtaned by the proposed RE scheme has hgher resoluton compared to the other RE schemes and s shown n Fgure 3(g). Table 1 shows the PSNR comparsons of the proposed technque over the other resoluton enhancement schemes lke DT-CWT- NLM,DT-CWT, DWT and SWT. 2014, IJCSMC All Rghts Reserved 1319
Table1 Comparson of PSNR of varous RE schemes Table1 shows that the Proposed DT-CWT-NLM-RE has hgher PSNR compared to the other schemes lke SWT-RE and DWT-RE,DT-CWT-RE and DT-CWT-NLM-RE. V. CONCLUSION A scheme for mage resoluton enhancement from a sngle low-resoluton mage usng the dual-tree complex wavelet transform(dt-cwt) wth NLM and curvelet transform has been proposed. Ths technque does curvelet denosng. The technque decomposes the resultant mage usng DT-CWT. Wavelet coeffcents and the LR nput mage were nterpolated usng the Lanczos nterpolator. DT-CWT s used as t s nearly shft nvarant and generates less artfacts, as compared wth DWT. NLM flterng s used to elmnate the artfacts generated by DT-CWT.The proposed technque s compared wth the conventonal RE schemes. The PSNR and vsual results shows the superor performance of proposed technque. [1] [Onlne].Avalable:http://www.satmagngcorp.com/ REFERENCES [2] Y. Pao, I. Shn, and H. W. Park, Image resoluton enhancement usng nter-subband correlaton n wavelet doman, n Proc. Int. Conf. Image Process., San Antono, TX, [3] C. B. Atkns, C. A. Bouman, and J. P. Allebach, Optmal mage scalng usng pxel classfcaton, n Proc. Int. Conf. Image Process., Oct. 7 10, 2001, pp. 864 867. [4] A. S. Glassner, K. Turkowsk, and S. Gabrel, Flters for common resamplng tasks, n Graphcs Gems. New York: Academc, 1990, pp. 147 165. [5] H. Demrel and G. Anbarafar, Dscrete wavelet transform-based satellte mage resoluton enhancement, IEEE Trans. Geosc. Remote Sens., vol. 49, no. 6, pp. 1997 2004, Jun. 2011. [6] H. Demrel and G. Anbarafar, Image resoluton enhancement by usng dscrete and statonary wavelet decomposton, IEEE Trans. Image Process., vol. 20, no. 5, pp. 1458 1460, May 2011. [7] I. W. Selesnck, R. G. Baranuk, and N. G. Kngsbur, The dual-tree complex wavelet transform, IEEE Sgnal Prcess. Mag., vol. 22, no. 6, pp. 123 151, Nov. 2005. [8] J. L. Starck, F. Murtagh, and J. M. Fadl, Sparse Image and Sgnal Processng: Wavelets, Curvelets, Morphologcal Dversty. Cambrdge, U.K.: Cambrdge Unv. Press, 2010. [9] M. Protter, M. Elad, H. Takeda, and P. Mlanfar, Generalzng the nonlocal-means to super-resoluton reconstructon, IEEE Trans. Image Process., vol. 18, no. 1, pp. 36 51, Jan. 2009. 2014, IJCSMC All Rghts Reserved 1320