Abstract
The study introduces image segmentation and denoising method which is based on geodesic framework and k means algorithm. Our method combines geodesic with k means algorithm. What’s more, a denoising method is applied to denoise. We optimize the distance function of k means algorithm to achieve our goals. This method can segment and denoise image which contains a lot of noise effectually.
Highlights
Many operations are needed in image, such as image annotation (Russell et al, 2008), image noise and image segmentation
Image noise and image segmentation are the basic operations among these operations
Bai and Sapiro (2007) introduced a distance function based on geodesic distance which solves the problem of Euclidean distance obviously
Summary
Many operations are needed in image, such as image annotation (Russell et al, 2008), image noise and image segmentation. Bai and Sapiro (2007) introduced a distance function based on geodesic distance which solves the problem of Euclidean distance obviously. What’s more, a method based on structural similarity and curvelet (He et al, 2013), an optimal weight method (Dinh et al, 2012) and a fast non-local means method (Xing et al, 2012) are used to solve image denoising. We use these two methods for reference and get a geodesic framework for image segmentation and denoising while improve them
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