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.
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References
Asadi Amiri S, Hassanpour H (2012) A preprocessing approach for image analysis using gamma correction. Int J Comput Appl 38(12):38–46
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
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
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
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
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
Hu YB, Jiang H, Li LB (2013) The research of application in image restoration based on wiener filtering. Appl Mech Mater 278:1232–1236
Hudson RD Jr (1969) Infrared system engineering. Wiley-Interscience, New York and London
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
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
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
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
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
Levin A, Weiss Y, Durand F, Freeman WT (2011) Understanding blind deconvolution algorithms. IEEE Trans Pattern Anal Mach Intell 33(12):2354–2367
Li C, Bovik AC (2010) Content-partitioned structural similarity index for image quality assessment. Signal Process Image Commun 25(7):517–526
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
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
Manap RA, Shao L (2015) Non-distortion-specific no-reference image quality assessment: a survey. Inf Sci 301:141–160
Mastriani M (2006) New wavelet-based superresolution algorithm for speckle reduction in SAR images. Int J Comput Sci 1(4):291–298
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
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
Ortiz A, Gorriz JM, Ramírez J, Salas-Gonzalez D (2013) Improving MRI segmentation with probabilistic GHSOM and multiobjective optimization. Neurocomputing 114:118–131
Polesel A, Ramponi G, Mathews VJ (2000) Image enhancement via adaptive unsharp masking. IEEE Trans Image Process 9(3):505–510
Pratt WK (1978) Digital Image Processing. Wiley, New York
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
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
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
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
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
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
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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
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DOI: https://doi.org/10.1007/s11042-019-7594-4
Keywords
- Un-sharp masking
- Image enhancement
- Gradient information of the block
- Blur image