An improved Smoothing Method Based on Diffusion Equation and K-means Clustering
When an image is acquired by a camera or other imaging system, the vision system for which it is intended is often unable to directly use it. The image may be corrupted because of random variations in intensity, variations in illumination, or poor contrast that must be dealt with in the early stages of vision processing. The main goal of this paper is to discuss partial differential equation methods for image enhancement aimed at eliminating these undesirable characteristics. We propose a improved filtering method, based on diffusion equation and k-means segmentation. The experimental results show that the proposed method has a better smoothing performance compared with the Perona-Malik method that uses the anisotropic diffusion. In addition, the proposed approach is simple and can provide a better smoothing in a few iterations, which gives, in a short execution time, a better image filtering.
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