De noising and Sharpening of Color Filter Captured Images using Adaptive Transformation

D. Anitha *, Ramya S.**
*-** Assistant Professor, Muthayammal Engineering College, Rasipuram, India
Periodicity:June - August'2013
DOI : https://doi.org/10.26634/jit.2.3.2403

Abstract

Obtaining cost-effective images with diagnosis quality is a current challenge, and this paper proposes a noise removal algorithm for color images corrupted by additive Gaussian noise and a robust open close sequence filter based on mathematical morphology for high probability additive Gaussian noise removal is given. First, an additive Gaussian noise detector using mathematical residues is to identify pixels that are contaminated by the additive Gaussian noise. Then the image is restored using specialized open-close sequence algorithms that apply only to the noisy pixels. But the color blocks that degrade the quality of the image will be recovered by a block smart erase method. This algorithm can be applied to highly corrupted images. Mathematical morphology is a nonlinear image processing methodology that is based on the application of lattice theory to spatial structures. In color images, algorithms are developed for boundary extraction via a morphological gradient operation and for region partitioning based on texture content. Mathematical morphological operations are useful in smoothing and sharpening, which often are useful as per or post processing steps.

Keywords

Adaptive Image Denoising, Adaptive Image Enhancement, Color Image Processing, Wavelet Domain

How to Cite this Article?

Anitha, D., and Ramya, S. (2013). Denoising And Sharpening Of Color Filter Captured Images Using Adaptive Transformation. i-manager’s Journal on Information Technology, 2(3), 1-6. https://doi.org/10.26634/jit.2.3.2403

References

[1] Basu, (2002). “Gaussian-based edge-detection methods: A survey,” IEEE Trans. Syst. Man Cybern. C, Appl. Rev., Vol. 32, No. 4, pp. 252–260.
[2]. B. Smolka and K.Plataniotis, (2002). “On the coupled forward and backward anisotropic diffusion scheme for color image enhancement”, in Proc. Int. Conf. Image and Video Retrieval.
[3]. B. Tang, G.Sapiro and V. Caselles, (2001). “Color image enhancement via chromaticity diffusion”, IEEE Trans. Image Process., Vol.10, No.5.
[4]. Claudio R. Jung and Jacob Scharcanski, (2009). “Sharpening Dermatological Color Images in the Wavelet domain”, IEEE, Vol.3, No.1, Feb 2009.
[5]. C. Naik and S.K.amd Murthy, “Hue-preserving color image enhancement without gamut problem”, IEEE Trans. Image Process, Vol. 12, No.12.
[6]. D. Demigny, (2002). “On optimal linear filtering for edge detection,” IEEETrans. Image Process, Vol. 11, No. 7, pp. 728–737.
[7]. D. Marr and E. Hildreth, “Theory of edge detection,” in Proc. Roy. Soc. Lond. A, Math. Phys. Sci. B, 1980, Vol. 207, pp. 187–217.
[8]. D. Tschumperle and R. Deriche, (2005). “Vectorvalued image regularization with PDEs: A common framework for different applications”, IEEE Trans. Pattern Anal. Mach. Intell., Vol.27, No.4.
[9]. H. Kaiqi, W.Qiao and W.Zhenyang, (2004). “Color image enhancement and evaluation algorithm based on human visual system”, In Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, Vol. 3.
[10]. J. F. Canny, (1986). “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell, Vol. 8, pp. 679–698.
[11]. J.L. Starck, F. Murtagh, E.J. Cands and D.L.Donoho, (2003). “Gray and color image contrast enhancement by the curvelet transform”, IEEE Trans. Image Process., Vol.12.
[12]. K.V. Velde, (1999). “Multi-Scale color image enhancement”, In Proc. IEEE int. Conf. Image processing, Vol.3.
[13]. P. Scheunders, (2004). “Wavelet thresholding of multivalued images”, IEEE Trans. Image process. Vol.13, No.4.
[14]. S. Murtaza, J.Ahmad, and U.Ali, (2006). “Efficient generalized colored image Enhancement”, In Proc. 2006 IEEE Conf. Cybernetics and Intelligent Systems, Vol.1.
[15]. S.H. Kim and J.P. Allebach, (2004). “Optimal unsharp mask for image sharpening and noise removal”, J. Electron. Imag. Vol. 14,.
[16]. Mittal, N. K., Ahmed, Mohd; Naaz, Farha, (2012). “Palmprint Recognition System Implementation Using 2D Gabor Filters”, JCIES Vol. 1, No 2, pp. 154-160(7).
[17]. S. Rahul Sharma, A. Anand Jain, Khushal Jain, and B. Persis Urbana Ivy, (2012). “Real Time Application of Superimposing Two 2-D Images and Their Projection on Screen”, J. Comput. Intell. Electron. Syst. 1, 54-56.
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