Abstract
Several areas like remote sensing, biomedical analysis, and computer vision require good image contrast and details for better interpretations and diagnoses’s. In the literature various image enhancement algorithms have been proposed to improve the perceptual aspects of the image for poorly contrasted images. The perceptual appearance of an image may be significantly improved by modifying the high-frequency components to have better edge and detail information in the image. The proposed scheme is a modification of simple unsharp mask image enhancement technique. Comparative analysis on standard images at different noise conditions shows that the proposed scheme, in general, outperforms the existing schemes.
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References
Jain AK (1989) Fundamentals of Image Processing. Prentice Hall, 2003
Gonzalez RC, Woods RE (1992) Digital Image Processing. Addison Wesley
Kim YT (1997) Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Transactions on Consumer Electronics 43(1):1–8
Wongsritong K, Kittayaruasiriwat K, Cheevasuvit F, Dejhan K, Somboonkaew A (1998) Contrast enhancement using multipeak histogram equalization with brightness preserving. Circuits and Systems, IEEE APCCAS
Brownrigg DRK (1984) The weighted median filter. Comm. ACM 27:807–818
Lee JS (1980) Digital image enhancement and noise filtering by using local statistics. IEEE Transactions on Pattern Analysis Machine Intelligence PAMI:165–168
Pizer SM, Amburn EP (1987) Adaptive histogram equalization and its variations. Computer Vision, Grpahics, and Image Processing 39:355–368
Patra JC, Panda G, Baliarsingh R (1994) Artificial neural network based nonlinearity estimation of pressure sensors. IEEE Trans Instrum Meas 3:874–81
Majhi B, Sa PK (2006) FLANN-based adaptive threshold selection for detection of impulsive noise in images. AEU – Inernational Journal of Electronics and Communication 61:478–484
Xu X, Miller EL (2002) Adaptive two-pass median filter to remove impulsive noise. Proceedings of International Conference on Image Processing 1:22–25
Chen T, Wu HR (2001) Adaptive impulse detection using center weighted median filters. IEEE Signal Process Letters 8:1–3
Kondo K, Haseyama M, Kitajima H (2002) An accurate noise detector for image restoration. Proceedings of International Conference in Image Processing I-321–I-324
Russo F (2004) Impulse noise cancellation in image data using a two-output nonlinear filters. Measurement 36:205–213
Khriji L, Gabbouj M (1998) Median-rational hybrid filters. Proceedings of International Conference on Image Processing 2:853–857
Crnojevic V, Senk V, Trpovski Z (2004) Advanced impulse detection based on pixel-wise MAD. IEEE Signal Processing Letters 11:589–592
Zhang S, Karim Md (2002) A new impulse detector for switching median filters. IEEE Signal Processing Letters 9:360–363
Alajlan N, Kamel M, Jernigan E (2004) Detail preserving impulsive noise removal. Signal Processing: Image Communication 19:993–1003
Wang Z, Zhang D (1999) Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Transactions on Circuits Systems–II: Analog Digital Signal Process 46:78–80
Frost VS, Stiles JA, Sharmugan KS, Holtzman JC (1982) A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Transactions on Pattern Analysis Machine Intelligence PAMI-4:157–165
Haykin S (2003) Neural Networks (2nd ed.). Prentice-Hall
Narendra PM, Fitch RC (1981) Real-time adaptive contrast enhancement. IEEE Transactions on Pattern Analysis Machine Intelligence PAMI(3):655–661
Patra JC, Pal RN, Chatterji BN, Panda G (1999) Identification of nonlinear dynamic systems using functional link artificial neural networks. IEEE Transactions on Systems Man Cybernet 9:254–262
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Mohapatra, S., Kumar Sa, P., Majhi, B. (2009). Impulsive noise removal image ehancement technique. In: Mastorakis, N., Sakellaris, J. (eds) Advances in Numerical Methods. Lecture Notes in Electrical Engineering, vol 11. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-76483-2_16
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DOI: https://doi.org/10.1007/978-0-387-76483-2_16
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