Anisotropic Diffusion-Based Unsharp Masking for Sharpness Improvement in Digital Images

Zohair Al-Ameen, Mayada A. Al-Healy, Rahma A. Hazim

Abstract


Various available imaging systems are capturing images with deficient sharpness due to numerous unavoidable shortcomings. Perceiving and extracting information from such images is uneasy. Hence, it is required to process these images properly to produce sharper and clearer details. Many methods exist that can be used to increase the sharpness of digital images. Among such, the unsharp mask has gained high popularity due to its rapidness and simplicity. Still, this filter usually degrades the processed images by an overshoot effect, which appears around the edges as white shades. In this study, an anisotropic diffusion-based unsharp mask filter, so-called ADUSM, is proposed, in that the degraded image is filtered using an amended anisotropic diffusion filter rather than processing it only by a low-pass Gaussian filter. This modification permitted the attenuation of the overshoot artefact which yielded to obtain better quality results. The ADUSM is tested with several types of images and assessed with two adequate quality metrics. Many experiments indicated that the proposed filter can outperform different existing methods and produce satisfactory results with reasonable application time.


Keywords


Image sharpening, Anisotropic diffusion, Image enhancement, Unsharp mask

Full Text:

Abstract PDF

References


Al-Ameen, Z., Muttar, A., & Al-Badrani, G. (2019). Improving the Sharpness of Digital Image Using an Amended Unsharp Mask Filter. International Journal of Image, Graphics and Signal Processing, 11(3), 1-9.

Banham, M. R., & Katsaggelos, A. K. (1997). Digital image restoration. IEEE signal processing magazine, 14(2), 24-41.

Calder, J., Mansouri, A., & Yezzi, A. (2010). Image sharpening via Sobolev gradient flows. SIAM Journal on Imaging Sciences, 3(4), 981-1014.

Cao, G., Zhao, Y., Ni, R., & Kot, A. C. (2011). Unsharp masking sharpening detection via overshoot artifacts analysis. IEEE Signal Processing Letters, 18(10), 603-606.

Carasso, A. S. (2003). The APEX method in image sharpening and the use of low exponent Lévy stable laws. SIAM Journal on Applied Mathematics, 63(2), 593-618.

Fu, S. J., Ruan, Q. Q., & Wang, W. Q. (2007b). A shock-diffusion equation with local coupling term for image sharpening. Journal of Optoelectronics Laser, 18(2), 245-253.

Fu, S., Ruan, Q., Wang, W., Gao, F., & Cheng, H. D. (2007a). A feature-dependent fuzzy bidirectional flow for adaptive image sharpening. Neurocomputing, 70(4-6), 883-895.

Gui, Z., & Liu, Y. (2011). An image sharpening algorithm based on fuzzy logic. Optik-International Journal for Light and Electron Optics, 122(8), 697-702.

Ibrahim, H., & Kong, N. S. P. (2009). Image sharpening using sub-regions histogram equalization. IEEE Transactions on Consumer Electronics, 55(2), 891- 895.

Kim, K. I., & Kwon, Y. (2010). Single-image super-resolution using sparse regression and natural image prior. IEEE transactions on pattern analysis and machine intelligence, 32(6), 1127-1133.

Kim, S. H., & Allebach, J. P. (2005). Optimal unsharp mask for image sharpening and noise removal. Journal of Electronic Imaging, 14(2), 023005-1- 023005-13.

Kim, S. H., & Allebach, J. P. (2005). Optimal unsharp mask for image sharpening and noise removal. Journal of Electronic Imaging, 14(2), 023005-1-023005-13.

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

Osher, S., & Rudin, L. I. (1990). Feature-oriented image enhancement using shock filters. SIAM Journal on numerical analysis, 27(4), 919-940.

Panetta, K., Zhou, Y., Agaian, S., & Jia, H. (2011). Nonlinear unsharp masking for mammogram enhancement. IEEE Transactions on Information Technology in Biomedicine, 15(6), 918-928.

Pardo-Igúzquiza, E., Chica-Olmo, M., & Atkinson, P. M. (2006). Downscaling cokriging for image sharpening. Remote Sensing of Environment, 102(1-2), 86-98.

Perona, P., & Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine intelligence, 12(7), 629-639.

Polesel, A., Ramponi, G., & Mathews, V. J. (2000). Image enhancement via adaptive unsharp masking. IEEE transactions on image processing, 9(3), 505-510.

Schavemaker, J. G., Reinders, M. J., Gerbrands, J. J., & Backer, E. (2000). Image sharpening by morphological filtering. Pattern Recognition, 33(6), 997-1012.

Toh, K., & Isa, N. (2011). Locally adaptive bilateral clustering for image deblurring and sharpness enhancement. IEEE Transactions on Consumer Electronics, 57(3), 1227-1235.

Webb, G. (1989). Sharpness issues in colour printing. The Journal of Photographic Science, 38(4-5), 173-176.

Wilscy, M., & Nair, M. S. (2008, June). A new method for sharpening color images using fuzzy approach. In International Conference Image Analysis and Recognition (pp. 65-74). Springer, Berlin, Heidelberg.

Yang, C. C. (2014). Finest image sharpening by use of the modified mask filter dealing with highest spatial frequencies. Optik-International Journal for Light and Electron Optics, 125(8), 1942-1944.

Zafeiridis, P., Papamarkos, N., Goumas, S., & Seimenis, I. (2016). A New Sharpening Technique for Medical Images using Wavelets and Image Fusion. Journal of Engineering Science & Technology Review, 9(3), 187–200.

Zhan, K., Shi, J., Teng, J., Li, Q., Wang, M., & Lu, F. (2017). Linking synaptic computation for image enhancement. Neurocomputing, 238, 1-12.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Copyright © 2014 Penerbit UTM Press. Universiti Teknologi Malaysia. All rights reserved.

Mailing Address: Penerbit UTM Press, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia.