Image segmentation based on the hybrid total variation model and the K-means clustering strategy
This work addresses image segmentation challenges for noisy or blurred images, but it is incremental as it builds on existing total variation and clustering techniques.
The paper tackled the problem of segmenting noisy or blurred images by proposing a two-step method combining a hybrid total variation model with K-means clustering, resulting in more efficient segmentation outcomes as shown in numerical comparisons.
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.