Abstract
Image segmentation plays an important role in various computer vision tasks. Nevertheless, noise always inevitably appears in images and brings a big challenge to image segmentation. To handle the problem, we study the nonlocal total variation (NLTV) spectral theory and build up an image segmentation framework with NLTV spectral transform to segment images with noise. Firstly, we decompose an image into the NLTV flow in the NLTV spectral transform, with which the max response time of each pixel is calculated. Secondly, a separation surface is constructed with the max response time to distinguish the objects and preserve the structure details in segmentation. Thirdly, the image is filtered by the surface in the NLTV spectral domain, and a rough segmentation result is obtained by means of an inverse transform. Finally, we use a binary process and morphological operations to refine the segmentation result. Experiments illustrate that our method can preserve edge structures effectively and has the ability to achieve competitive segmentation performance compared with the state-of-the-art approaches.
Highlights
Image segmentation refers to partitioning images into multiple homogeneous parts or objects
E parameter settings for the proposed method are as follows: experiments show that when the image is transformed into the nonlocal total variation (NLTV) domain, detailed information is located in a low time scale
We have analyzed the properties of NLTV spectral transform with the help of theoretical proof and experiments
Summary
Image segmentation refers to partitioning images into multiple homogeneous parts or objects. The NLTV spectral transform can preserve image structures because of its nonlocal operators [28]. Inspired by the work [29], we demonstrate the sensitivity of the NLTV spectral transform to size, contrast, and its detailed structures in images with or without noise. Irdly, the image is filtered by the surface in the NLTV spectral domain, followed by the NLTV inverse transform to obtain a rough segmentation result. (ii) We propose an image segmentation framework using NLTV spectral transform, which fits a separation surface to filter sub-bands in the NLTV spectral domain, and it obtains segmentation results by means of postprocessing. A segmentation method using NLTV spectral transform for images with noise is introduced. We corrupt the images with Gaussian noise, Salt & Pepper noise, and Speckle noise
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