Skip to main content
Log in

An adaptive block based un-sharp masking for image quality enhancement

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

An image may suffer from some degradation such as blurriness. This degradation affects the image contrast. There are various approaches to improve the contrast of the images. Among these approaches, un-sharp masking is a popular method due to its simplicity in implementation and computation. In the un-sharp masking method, the details of the input image are boosted to improve the image quality. In this method, the quality of the enhanced image directly depends on the parameter named gain factor. Since the quality of an image may not be the same throughout the image, in this paper we propose an adaptive un-sharp masking method to locally improve the quality of the images. In this method, at first, the input image is divided into a number of overlapping blocks. Then the appropriate gain factor is estimated for the pixels of each block using the gradient information of the block. Subjective and objective image quality assessments are used to compare the performance of the proposed method with both the classic and the recently developed un-sharp masking methods. The experimental results show that the proposed method has a better performance in comparison to the other existing methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Asadi Amiri S, Hassanpour H (2012) A preprocessing approach for image analysis using gamma correction. Int J Comput Appl 38(12):38–46

    Google Scholar 

  2. Asadi Amiri S, Hassanpour H, Marouzi OR (2017) No-reference image quality assessment based on localized discrete cosine transform for JPEG compressed images. Multimed Tools Appl 77(1):787–803

    Article  Google Scholar 

  3. Askari Javaran T, Hassanpour H, Abolghasemi V (2017) Automatic estimation and segmentation of partial blur in natural images. Vis Comput Int J Comput Graph 33(2):151–161

    Google Scholar 

  4. Chitwong S, Phahonyothing S, Nilas P, Cheevasuvit F (2006) Contrast enhancement of satellite image based on adaptive unsharp masking using wavelet transform. In: ASPRS 2006 Annual Conference, Reno, Nevada

  5. Gupta R, Bhateja V (2012) An improved unsharp masking algorithm for enhancement of mammographic masses. In: IEEE Students Conference on Engineering and Systems (SCES), pp 1–4

  6. Hajian A, Ramli DA (2018) Sharpness enhancement of finger-vein image based on modified un-sharp mask with log-Gabor filter. Procedia Computer Science 126:431–440

    Article  Google Scholar 

  7. Hu YB, Jiang H, Li LB (2013) The research of application in image restoration based on wiener filtering. Appl Mech Mater 278:1232–1236

    Article  Google Scholar 

  8. Hudson RD Jr (1969) Infrared system engineering. Wiley-Interscience, New York and London

    Google Scholar 

  9. Jane O, Ilk HG (2010) Priority and significance analysis of selecting threshold values in adaptive unsharp masking for infrared images. In: IEEE International Conference on Microwave Techniques (COMITE), pp 9–12

  10. Joseph A, BPatil S (2015) Restoration and comparisons of Gaussian blurred- Noisy image using different filtering techniques. International Journal of Science and Research (IJSR) 4:2576–2580

    Google Scholar 

  11. Kwok N, Shi H (2014) Design of unsharp masking filter kernel and gain using particle swarm optimization. In: IEEE International Congress on Image and Signal Processing (CISP), pp 217–222

  12. Lanchi X, Jingjing G, Zhihui L (2015) A Novel Unsharp Mask Sharpening Method in Preprocessing for Face Recognition. In: Fifth IEEE International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), pp 378–381

  13. Larson EC, Chandler DM (2010) Most apparent distortion: full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging (JEI) 19(1):011006-1–011006-21

    Google Scholar 

  14. Levin A, Weiss Y, Durand F, Freeman WT (2011) Understanding blind deconvolution algorithms. IEEE Trans Pattern Anal Mach Intell 33(12):2354–2367

    Article  Google Scholar 

  15. Li C, Bovik AC (2010) Content-partitioned structural similarity index for image quality assessment. Signal Process Image Commun 25(7):517–526

    Article  Google Scholar 

  16. Lin SCF, Wong CY, Jiang G, Rahman MA, Ren TR, Kwok N, Shi H, Yu YH, Wu T (2016) Intensity and edge based adaptive unsharp masking filter for color image enhancement. Optik 127(1):407–414

    Article  Google Scholar 

  17. Mai CLDA, Nguyen MTT, Kwok NM (2011) A modified unsharp masking method using particle swarm optimization. In: IEEE International Congress on Image and Signal Processing (CISP), vol 2, pp 646–650

  18. Manap RA, Shao L (2015) Non-distortion-specific no-reference image quality assessment: a survey. Inf Sci 301:141–160

    Article  Google Scholar 

  19. Mastriani M (2006) New wavelet-based superresolution algorithm for speckle reduction in SAR images. Int J Comput Sci 1(4):291–298

    Google Scholar 

  20. Mortezaie Z, Hassanpour H, Asadi Amiri S (2017) Image enhancement using an adaptive un-sharp masking method considering the gradient variation. International Journal of Engineering (IJE) 30(8):1118–1125

    Google Scholar 

  21. Mortezaie Z, Hassanpour H, Asadi Amiri S (2017) Contrast enhancement in digital images using an adaptive Unsharp masking method. World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering 11(9):981–986

    Google Scholar 

  22. Ortiz A, Gorriz JM, Ramírez J, Salas-Gonzalez D (2013) Improving MRI segmentation with probabilistic GHSOM and multiobjective optimization. Neurocomputing 114:118–131

    Article  Google Scholar 

  23. Polesel A, Ramponi G, Mathews VJ (2000) Image enhancement via adaptive unsharp masking. IEEE Trans Image Process 9(3):505–510

    Article  Google Scholar 

  24. Pratt WK (1978) Digital Image Processing. Wiley, New York

    MATH  Google Scholar 

  25. Sharma S, Sharma S, Mehra R (2013) Image restoration using modified Lucy Richardson algorithm in the presence of Gaussian and motion blur. In: Advance in Electronic and Electric Engineering, vol 3, issue 8, pp 1063–1070

  26. Tustison NJ, Shrinidhi KL, Wintermark M, Durst CR, Kandel BM, Gee JC, Grossman MC, Avants BB (2015) Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. Neuroinformatics 13(2):209–225

    Article  Google Scholar 

  27. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  28. Ying L, Ming NT, Keat LB (2008) A wavelet based image sharpening algorithm. IEEE International Conference on Computer Science and Software Engineering 1:1053–1056

    Google Scholar 

  29. Zaafouri A, Sayadi M, Fnaiech F (2011) A developed unsharp masking method for images contrast enhancement. In: IEEE International Multi-Conference on Systems, Signals and Devices (SSD), pp 1–6

  30. Zhang M, Zou F, Zheng J (2017) The Linear Transformation Image Enhancement Algorithm Based on HSV Color Space. In: International Conference on Advances Intelligent Information Hiding and Multimedia Signal Processing, Smart innovation, Systems and technologies, pp 19–27

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. Asadi Amiri.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mortezaie, Z., Hassanpour, H. & Asadi Amiri, S. An adaptive block based un-sharp masking for image quality enhancement. Multimed Tools Appl 78, 23521–23534 (2019). https://doi.org/10.1007/s11042-019-7594-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7594-4

Keywords

Navigation