International Science Index


An Image Enhancement Method Based on Curvelet Transform for CBCT-Images


Image denoising plays extremely important role in digital image processing. Enhancement of clinical image research based on Curvelet has been developed rapidly in recent years. In this paper, we present a method for image contrast enhancement for cone beam CT (CBCT) images based on fast discrete curvelet transforms (FDCT) that work through Unequally Spaced Fast Fourier Transform (USFFT). These transforms return a table of Curvelet transform coefficients indexed by a scale parameter, an orientation and a spatial location. Accordingly, the coefficients obtained from FDCT-USFFT can be modified in order to enhance contrast in an image. Our proposed method first uses a two-dimensional mathematical transform, namely the FDCT through unequal-space fast Fourier transform on input image and then applies thresholding on coefficients of Curvelet to enhance the CBCT images. Consequently, applying unequal-space fast Fourier Transform leads to an accurate reconstruction of the image with high resolution. The experimental results indicate the performance of the proposed method is superior to the existing ones in terms of Peak Signal to Noise Ratio (PSNR) and Effective Measure of Enhancement (EME).

[1] Kumar, Manoj, and Manoj Diwakar. "CT image denoising using locally adaptive shrinkage rule in tetrolet domain." Journal of King Saud University-Computer and Information Sciences (2016).
[2] Alam, Md Mushfiqul, Tamanna Howlader, and SM Mahbubur Rahman. "Entropy-based image registration method using the curvelet transform". Signal, Image and Video Processing 8.3 (2014): 491-505.
[3] Guo, Qi, and Xiaoyun Su. "The study of medical image enhancement based on curvelet\ footnote {This work is supported by Natural Science Foundation of Heilongjiang Province (No. GFQQ2440501411).}." Technology and Health Care 23.s2 (2015): S319-S323.
[4] Babisha, B. R., and R. Vijayarajan. "Multiscale Image Fusion Using the Curvelet Transform and Non Orthogonal Filter Bank." (2015).
[5] Ma, Jianwei, and Gerlind Plonka. "A review of curvelets and recent applications." IEEE Signal Processing Magazine 27.2 (2010): 118-133.
[6] Gonzalez, R.C., Woods, R.E.: Digital Image Processing. 2nd ed. Pearson Education, Singapore (2002).
[7] Swami, Preety D., and Alok Jain. "Image denoising by supervised adaptive fusion of decomposed images restored using wave atom, curvelet and wavelet transform." Signal, Image and Video Processing 8.3 (2014): 443-459.
[8] Al-Hammadi, Muneer H., et al. "Curvelet transform and local texture based image forgery detection." International Symposium on Visual Computing. Springer Berlin Heidelberg, 2013.
[9] Himanshi, Bhateja V., Abhinav Krishn, and Akanksha Sahu. "Medical Image Fusion in Curvelet Domain Employing PCA and Maximum Selection Rule." Proc. (Springer) 2nd International Conference on Computers and Communication Technologies (IC3T-2015), Hyderabad, India. Vol. 1. 2015.
[10] Lari, Mohammad Reza Akbarzadeh, Sedigheh Ghofrani, and Des McLernon. "Using Curvelet transform for watermarking based on amplitude modulation."Signal, Image and Video Processing 8.4 (2014): 687-697.
[11] Wu, Haibo, Andreas Maier, and Joachim Hornegger. "Iterative CT Reconstruction Using Curvelet-Based Regularization." Bildverarbeitung für die Medizin 2013. Springer Berlin Heidelberg, 2013. 229-234.
[12] Dhahbi, Sami, Walid Barhoumi, and Ezzeddine Zagrouba. "Breast cancer diagnosis in digitized mammograms using curvelet moments." Computers in biology and medicine 64 (2015): 79-90.
[13] Diwakar, Manoj, and Manoj Kumar. "A Hybrid Method Based CT Image Denoising Using Nonsubsampled Contourlet and Curvelet Transforms." Proceedings of International Conference on Computer Vision and Image Processing. Springer, Singapore, 2017.
[14] Swami, P. D., Jain, A., Singhai, J.: A multilevel Shrinkage approach for curvelet denoising. In: Proceeding of International Conference on Information and Multimedia Technology, pp. 268–272, Jeju Island, Korea (Dec. 16–18, 2009).
[15] Al-Hammadi, Muneer H., et al. "Curvelet transform and local texture based image forgery detection." International Symposium on Visual Computing. Springer Berlin Heidelberg, 2013.
[16] Jin, Jing, et al. "Curvelet transform based adaptive image deblocking method." Computers & Electrical Engineering 40.8 (2014): 117-129.
[17] Candes, Emmanuel, et al. "Fast discrete curvelet transforms." Multiscale Modeling & Simulation 5.3 (2006): 861-899.
[18] Candes, Emmanuel J., and David L. Donoho. "Continuous curvelet transform: II. Discretization and frames." Applied and Computational Harmonic Analysis 19.2 (2005): 198-222.
[19] Candès, Emmanuel J., and David L. Donoho. "New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities." Communications on pure and applied mathematics 57.2 (2004): 219-266.
[20] Ma, Jianwei, and Gerlind Plonka. "The curvelet transform." IEEE signal processing magazine 27.2 (2010): 118-133.
[21] Curvelet. CurveLab 2.1.2 (April 2008). (Online). (Accessed 6th May 2016). Available at:
[22] OSRIX. DICOM image sample sets, INCISIX, Dental Scan, CT 64. (Online). (Accessed 25th April 2016]. Available at:
[23] Wang, Zhou, et al. "Image quality assessment: from error visibility to structural similarity." IEEE transactions on image processing 13.4 (2004): 600-612.
[24] Akila, K., L. S. Jayashree, and A. Vasuki. "Mammographic image enhancement using indirect contrast enhancement techniques–a comparative study." Procedia Computer Science 47 (2015): 255-261.
[25] Agaian, Sos S., Karen Panetta, and Artyom M. Grigoryan. "Transform-based image enhancement algorithms with performance measure." IEEE Transactions on Image Processing 10.3 (2001): 367-382.