- Volume 8 Issue 11
Thanks to the multimedia technology development, it is possible to show image representations in high quality and to process images in various ways. Anisotropic diffusion as an effective diffusion filtering among many image preprocessing methods and postprocessing methods is used in reduction of speckle noises of ultrasound images, image restoration, edge detection, and image segmentation. However, the conventional anisotropic diffusion based on a cross-kernel causes the following problems. The problem is the concentration of edges in the vertical or horizontal directions. In this paper, a new anisotropic diffusion transform based on directions of gradient is proposed. The proposed method uses the eight directional square-kernel which is an expanded form of the cross-kernel. The proposed method is to select directions of small gradient based on square-kernel. Therefore, the range of proposed diffusion is selected adaptively according to the number of the directions of gradient. Experimental results show that the proposed method can decrease the concentration of edges in the vertical or horizontal directions, remove impulse noise. The image in high quality can be obtained as a result of the proposed method.
- J. Babaud, A. Witkin, M. Baudin, and R. Duda, "Uniqueness of the gaussian kernel for scale-space filtering," IEEE Trans. Pattern Anal. and Machine Intell., Vol. PAMI-8, 1986(1). https://doi.org/10.1109/TPAMI.1986.4767749
- P. Perona and J. Malik, "Scale-Space and Edge Detection Using Anisotropic Diffusion," IEEE Trans. Pattern Anal. and Machine Intell., Vol.12, No.7, pp.629-639, 1990. https://doi.org/10.1109/34.56205
- M. J. Black, G. Sapiro, D. H. Marimont, and D. Hegger, "Robust Anisotropic Diffusion," IEEE Trans. Image Processing, Vol.7, No.3, pp.421-432, 1998(3). https://doi.org/10.1109/83.661192
- F. Voci, S. Eiho, N. Sugimoto, and H. Sekiguchi, "Estimating the Gradient Threshold in the Perona-Malik Equation," IEEE Signal Processing Magazine, pp.39-46, 2004(5). https://doi.org/10.1109/MSP.2004.1296541
- J. Canny, "A Computational Approach to Edge Detection," IEEE Trans. Pattern Anal. and Machine Intell., Vol. PAMI-8, pp.679-698, 1986. https://doi.org/10.1109/TPAMI.1986.4767851
- H. Y. Kim, "Gradient Histogram-Based Anisotropic Diffusion," Personal Communication, 2006.
- K. Karl, C. F. Westin, K. Ron, and V. Kirby, "Oriented Speckle Reducing Anisotropic Diffusion," IEEE Transactions on Image Processing, Vol.16, No.5, pp.1412-1424, 2007. https://doi.org/10.1109/TIP.2007.891803
- W. Yi, Z. Liangpei, and L. Pingxiang, "Local Variance Controlled Forward and Backward Diffusion for Image Enhancement and Noise Reduction," IEEE Transactions on Image Processing, Vol.16, pp.1854-1864, 2007(7). https://doi.org/10.1109/TIP.2007.899002
- P. Zhigeng and L. Jianfeng, "A Bayes-Based Region-Growing Algorithm for Medical Image Segmentation," Computing in Science & Engineering, Vol.9, pp.32-38, 2007.
- A. K. W. Sum and P. Y. S. Cheung, "Stabilized Anisotropic Diffusions," IEEE International Conference Acoustics, Speech and Signal Processing, Vol.1, pp.709-712, 2007(4). https://doi.org/10.1109/ICASSP.2007.366006