Abstract
The performance of image segmentation highly relies on the original inputting image. When the image is contaminated by some noises or blurs, we can not obtain the efficient segmentation result by using direct segmentation methods. In order to efficiently segment the contaminated image, this paper proposes a two step method based on the hybrid total variation model with a box constraint and the K-means clustering method. In the first step, the hybrid model is based on the weighted convex combination between the total variation functional and the high-order total variation as the regularization term to obtain the original clustering data. In order to deal with non-smooth regularization term, we solve this model by employing the alternating split Bregman method. Then, in the second step, the segmentation can be obtained by thresholding this clustering data into different phases, where the thresholds can be given by using the K-means clustering method. Numerical comparisons show that our proposed model can provide more efficient segmentation results dealing with the noise image and blurring image.
Highlights
Image segmentation has become increasingly important in the last decade, due to a fast expanding field of applications in image analysis and computer vision
The most well-known regionbased model is the Mumford and Shah (MS) model [32], in which an image is decomposed into a set of regions within the bounded open set and these regions are separated by smooth edges
Once the solution is obtained, we set it as the inputting data in the second step and use the K-means clustering method to threshold it into expected phases
Summary
Image segmentation has become increasingly important in the last decade, due to a fast expanding field of applications in image analysis and computer vision. During some phases of obtaining a real image, we can only get a contaminated image due to the interference of some random noises and blurs This interference leads not to obtain an expected segmentation results by using classical methods such as the MS model and clustering methods. Once the restored image is obtained, the second stage is to threshold it into different phases by using the K-means clustering method. Once the solution is obtained, we set it as the inputting data in the second step and use the K-means clustering method to threshold it into expected phases.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
