Improving the Sharpness of Digital Images Using a Modified Laplacian Sharpening Technique

Zohair Al-Ameen, Shamil Al-Ameen, Ahmed Al-Othman

Abstract


Many imaging systems produce images with deficient sharpness due to different real limitations. Hence, various image sharpening techniques have been used to improve the acutance of digital images. One of such is the well-known Laplacian sharpening technique. When implementing the basic Laplacian technique for image sharpening, two main drawbacks were detected. First, the amount of introduced sharpness cannot be increased or decreased. Second, in many situations, the resulted image suffers from a noticeable increase in brightness around the sharpened edges. In this article, an improved version of the basic Laplacian technique is proposed, wherein it contains two key modifications of weighting the Laplace operator to control the introduced sharpness and tweaking the second order derivatives to provide adequate brightness for recovered edges. To perform reliable experiments, only real-degraded images were used, and their accuracies were measured using a specialized no-reference image quality assessment metric. From the obtained experimental results, it is evident that the proposed technique outperformed the comparable techniques in terms of recorded accuracy and visual appearance.

Keywords


Image sharpening; Laplace operator; Modified Laplacian; Second-order derivatives

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References


L. Juncheng, “Edge detection using 1~2-order fractional differential mask”, Computer Engineering and Applications, vol. 50, no. 21, pp. 14-18, 2014.

C. Dharmaraj, M. Krishna and R. Murugesan, “A Feature Identification System for Electron Magnetic Resonance Tomography: Fusion of Principal Components Transform, Color Quantization and Boundary Information”, Journal of Mathematical Imaging and Vision, vol. 30, no. 3, pp. 284-297, 2008.

H. Kotera and H. Wang, “Multiscale image sharpening adaptive to edge profile”, Journal of Electronic Imaging, vol. 14, no. 1, pp. 013002-1–013002-17, 2005.

Z. Afrose and Y. Shen, “Mesh color sharpening”, Advances in Engineering Software, vol. 91, pp. 36-43, 2016.

S. Fu, Q. Ruan, W. Wang, F. Gao and H. Cheng, “A feature-dependent fuzzy bidirectional flow for adaptive image sharpening”, Neurocomputing, vol. 70, no. 4-6, pp. 883-895, 2007.

T. Ma, L. Li, S. Ji, X. Wang, Y. Tian, A. Al-Dhelaan and M. Al-Rodhaan, “Optimized Laplacian image sharpening algorithm based on graphic processing unit”, Physica A: Statistical Mechanics and its Applications, vol. 416, pp. 400-410, 2014.

M. Millán and E. Valencia, “Color image sharpening inspired by human vision models”, Applied Optics, vol. 45, no. 29, pp. 7684-7697, 2006.

K. Panetta, Y. Zhou, S. Agaian and H. Jia, “Nonlinear Unsharp Masking for Mammogram Enhancement”, IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 6, pp. 918-928, 2011.

D. Fang, Z. Nanning and X. Jianru, “Image smoothing and sharpening based on nonlinear diffusion equation”, Signal Processing, vol. 88, no. 11, pp. 2850-2855, 2008.

S. Anand, R. Shantha Selva Kumari, T. Thivya and S. Jeeva, “Sharpening enhancement of ultrasound images using contourlet transform”, Optik - International Journal for Light and Electron Optics, vol. 124, no. 21, pp. 4789-4792, 2013.

S. Anand and R. Shantha Selva Kumari, “Sharpening enhancement of Computed Tomography (CT) images using Hyperbolic Secant Square filter”, Optik - International Journal for Light and Electron Optics, vol. 124, no. 15, pp. 2121-2124, 2013.

Z. Gui and Y. Liu, “An image sharpening algorithm based on fuzzy logic”, Optik - International Journal for Light and Electron Optics, vol. 122, no. 8, pp. 697-702, 2011.

S. Bettahar and A. Stambouli, “Shock filter coupled to curvature diffusion for image denoising and sharpening”, Image and Vision Computing, vol. 26, no. 11, pp. 1481-1489, 2008.

J. Schavemaker, M. Reinders, J. Gerbrands and E. Backer, “Image sharpening by morphological filtering”, Pattern Recognition, vol. 33, no. 6, pp. 997-1012, 2000.

N. Narvekar and L. Karam, “A No-Reference Image Blur Metric Based on the Cumulative Probability of Blur Detection (CPBD)”, IEEE Transactions on Image Processing, vol. 20, no. 9, pp. 2678-2683, 2011.

S. Osher and L. Rudin, “Feature-oriented image enhancement using shock filters”, SIAM Journal on Numerical Analysis, vol. 27, no. 4, pp. 919-940, 1990.




DOI: http://dx.doi.org/10.12962/j20882033.v29i2.3356

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