Image Filtering and Edge Detection - Techniques and Applications

Authors

  • Bhaludra R Nadh Singh   Professor of CSE & Head, Department of Computer Science and Engineering, Bhoj Reddy Engineering College for Women, Vinay Nagar, Hyderabad. Telangana, India

Keywords:

Image Denoising, Edge Detection, Image Processing, Noise Filters

Abstract

Image denoising is the manipulation of the image data to produce a visually high-quality image. In this paper, we give a brief overview of various noise models. These noise models can be selected by analysis of their origin. Noise removal is an important task in image processing. In general, the results of the noise removal have a strong influence on the quality of the image processing techniques. The nature of the noise removal problem depends on the type of noise corrupting the image. Edge detection is an image processing technique for finding the boundaries of objects within images. It works by detecting discontinuities in brightness. Edge detection is used for image segmentation and data extraction in areas such as image processing, computer vision, and machine vision. Edge detection is an important technique in many image processing applications such as object recognition, motion analysis, pattern recognition, medical image processing etc. This paper presents a review on different noises, Noise Filters and Edge detection of image processing.

References

  1. J. Wu, C. Tang PDE-based random-valued impulse noise removal based on a new class of controlling functions IEEE Trans. Image Process., 2 (9) (2011), pp. 2428-2438
  2. A.S. Awad Standard deviation for obtaining the optimal direction in the removal of impulse noise IEEE Signal Process. Lett., 18 (7) (2011), pp. 407-410
  3. U. Ghanekar, A.K. Singh, R. Pandey A contrast enhancement-based filter for removal of random valued impulse noise IEEE Signal Process. Lett., 17 (1) (2010), pp. 47-50
  4. V. Crnojevíc, V. ˇSenk, ˇZ. Trpovski Advanced impulse detection based on pixel-wise MAD IEEE Signal Process. Lett., 11 (7) (2004), pp. 589-592
  5. C. Tomasi, R. Manduchi Bilateral filtering for gray and colour images IEEE Int. Conf. Computer Vision (1998), pp. 839-846.
  6. P. Perona, J. Malik Scale -space and edge detection using anisotropic diffusion IEEE Trans. Pattern Anal. Machine Intell., 12 (1990), pp. 629-639
  7. H. Takeda, S. Farsiu, P. Milanfar Kernel regression for image processing and reconstruction IEEE Trans. Image Process., 16 (2) (2007), pp. 349-366
  8. A. Buades, B. Coll, J. Morel A non-local algorithm for image denoising IEEE International Conference on Computer Vision and Pattern Recognition (2005).
  9. [9]K. Dabov, A. Foi, V. Katkovnik, K”. Egiazarian Image denoising by sparse 3D transform-domain collaborative filtering IEEE Trans. Image Process., 16 (8) (2007), pp. 2080-2095
  10. L. Zhang, W. Dong, D. Zhang, G. Shi Two-stage image denoising by principal component analysis with local pixel grouping Pattern Recognition, 43 (4) (2010), pp. 1531-1549
  11. C.A. Deledalle, J. Salmon, A.S. Dalalyan Image denoising with patch based PCA: local versus global Proceedings of the British Machine Vision Conference (BMVC), 63 (3) (2011), pp. 782-789
  12. T. Tasdizen, Principal components for non-local means image denoising, Proc Int Conf Image, ICIP, 2008. pp. 1728–1731.
  13. Y. Lin, J. Cai, A new threshold function for signal denoising based on wavelet transform, in: Proc. IEEE Int. Conf. Meas. Technol. Mechatronics Autom., pp. 200–203, Mar. 2010.
  14. Y. Ding, I.W. Selesnick Artifact-free wavelet denoising: Non-convex sparse regularisation, convex optimization IEEE Signal Process. Lett., 22 (9) (2015), pp. 1364-1368
  15. A.E. Cetin, M. Tofighi Projection-based wavelet denoising [lecture notes] IEEE Signal Process. Mag., 32 (5) (2015), pp. 120-124
  16. Z. Madadi, G.V. Anand, A.B. Premkumar Signal detection in generalised Gaussian noise by linear wavelet denoising IEEE Trans. Syst., 60 (11) (2013), pp. 2973-2986
  17. R. Hussein, K.B. Shaban, A.H. El-Hag, Histogram-based thresholding in discrete wavelet transform for partial discharge signal denoising, in: Proc. Int. Conf. Commun., Signal Process., pp. 1– 5, Appl. (IC CSPA), 2015.
  18. L.Z. Manor, K. Rosenblum, Y.C. Eldar Dictionary optimization for block-sparse representations IEEE Trans. Signal Process., 60 (5) (2012), pp. 2386-2395
  19. S. Dang, Y. Zhang, D. Dong, A patch-based non-local means method for image denoising, ScIDE'12 Proceedings of the third Sino Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering, pp. 582–589, 2012.
  20. P. Chatterjee, P. Milanfar Patch-based near-optimal image denoising IEEE Trans. Image Process., 21 (4) (2012), pp. 1635-1649
  21. L. Xu, J. Li, Y. Shu, P. Junhuan SAR image denoising via clustering based principal component analysis IEEE Trans. Geosci. Remote Sensing, 52 (11) (2014), pp. 6858-6869.

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Published

2023-11-30

Issue

Section

Research Articles

How to Cite

[1]
Bhaludra R Nadh Singh , " Image Filtering and Edge Detection - Techniques and Applications" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 6, pp.382-396, November-December-2023.