Commenced in January 2007
Paper Count: 30132
Contrast Enhancement in Digital Images Using an Adaptive Unsharp Masking Method
Abstract:Captured images may suffer from Gaussian blur due to poor lens focus or camera motion. Unsharp masking is a simple and effective technique to boost the image contrast and to improve digital images suffering from Gaussian blur. The technique is based on sharpening object edges by appending the scaled high-frequency components of the image to the original. The quality of the enhanced image is highly dependent on the characteristics of both the high-frequency components and the scaling/gain factor. Since the quality of an image may not be the same throughout, we propose an adaptive unsharp masking method in this paper. In this method, the gain factor is computed, considering the gradient variations, for individual pixels of the image. Subjective and objective image quality assessments are used to compare the performance of the proposed method both with the classic and the recently developed unsharp masking methods. The experimental results show that the proposed method has a better performance in comparison to the other existing methods.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132138Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 588
 J. N. Archana, and Dr. P. Aishwarya, "A review on the image sharpening algorithms using unsharp masking," International Journal of Engineering Science and Computing, vol. 6, no. 7, 2016, pp. 8729-8733.
 F. Vankawala, A. Ganatra, and A. Patel, "A Survey on different Image Deblurring Techniques," International Journal of Computer Applications, 116(13), 2015, pp. 15-18.
 S. Rajput, and S. R. Suralkar, "Comparative study of image enhancement techniques," International Journal of Computer Science and Mobile Computing-A Monthly Journal of Computer Science and Information Technology, 2(1), 2013, pp. 11-21.
 C. Song, H. Deng, H. Gao, H. Zhang, and W. Zuo, "Bayesian non-parametric gradient histogram estimation for texture-enhanced image deblurring," Neurocomputing, 197, 2016, pp. 95-112.
 G. Wang, J. Xu, Z. Pan, Q. Dong, Z. Zhang, and S. Zheng, "Motion deblurring using normalized nonlinear diffusion regularization," Optik-International Journal for Light and Electron Optics, 126(24), 2015, pp. 4981-4986.
 A. Polesel, G. Ramponi, and V.J. Mathews, "Image enhancement via adaptive unsharp masking," IEEE transactions on image processing 9, no. 3, 2000, pp. 505-510.
 O. Jane, and H.G. Ilk, "Priority and significance analysis of selecting threshold values in Adaptive Unsharp Masking for infrared images," In Microwave Techniques (COMITE), 2010 15th International Conference on, 2010, pp. 9-12.
 A. Zaafouri, M. Sayadi, and F. Fnaiech, "A developed unsharp masking method for images contrast enhancement," In Systems, Signals and Devices (SSD), 8th International Multi-Conference on, 2011, pp. 1-6.
 L. Ying, N.T. Ming, and L.B. Keat, "A wavelet based image sharpening algorithm," In Computer Science and Software Engineering, 2008 International Conference on, vol. 1, 2008, pp. 1053-1056.
 S. Chitwong, S. Phahonyothing, P. Nilas, and F. Cheevasuvit, "Contrast enhancement of satellite image based on adaptive unsharp masking using wavelet transform,", In ASPRS 2006 Annual Conference, Reno, Nevada, 2006.
 C. L. D. A. Mai, M. T. T. Nguyen, and N. M. Kwok, "A modified unsharp masking method using particle swarm optimization," In Image and Signal Processing (CISP), 2011 4th International Congress on, vol. 2, 2011, pp. 646-650.
 N. Kwok, and H. Shi, "Design of unsharp masking filter kernel and gain using particle swarm optimization," In Image and Signal Processing (CISP), 2014 7th International Congress on, 2014, pp. 217-222.
 S.C.F. Lin, C.Y. Wong, G. Jiang, M.A. Rahman, T.R. Ren, N. Kwok, H. Shi, Y.H. Yu, and T. Wu, "Intensity and edge based adaptive unsharp masking filter for color image enhancement," Optik-International Journal for Light and Electron Optics 127, no. 1, 2016, pp. 407-414.
 M. Zhang, F. Zou, and J. Zheng, "The linear transformation image enhancement algorithm based on HSV color space," In Advances in Intelligent Information Hiding and Multimedia Signal Processing: Proceeding of the Twelfth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Nov., 21-23, 2016, Kaohsiung, Taiwan, Vol. 2, 2017, pp. 19-27. Springer International Publishing.
 W. K. Pratt, "Digital Image Processing," Wiley, New York, 1978.
 Y. Yu, and S.T. Acton, "Speckle reducing anisotropic diffusion," IEEE Transactions on image processing 11, no. 11, 2002, pp. 1260-1270.
 Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE transactions on image processing 13, no. 4, 2004, pp. 600-612.
 C. Li, and A. C. Bovik, "Content-partitioned structural similarity index for image quality assessment," Signal Processing: Image Communication 25, no. 7, 2010, pp. 517-526.
 E. C. Larson, and D. M. Chandler, "Most apparent distortion: full-reference image quality assessment and the role of strategy," Journal of Electronic Imaging 19, no. 1, 2010, pp. 011006-1 - 011006-21.