A Robust Image Segmentation Framework Based on Nonlocal Total Variation Spectral Transform
Jianwei Zhang, Yue Shen, Zhaohui Zheng, Le Sun · Wireless Communications and Mobile Computing · 2022
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.