Deep Learning Image Segmentation Based on Adaptive Total Variation Preprocessing

Abstract

This article proposes a two-stage image segmentation method based on the MS model, aiming to enhance the segmentation accuracy of images with complex structure and background. In the first stage, in order to obtain the smooth approximate solution of the image by minimizing the energy functional, an anisotropic regularization term formed by the combination of the gradient operator and an adaptive weighted matrix is introduced. Different weights in both horizontal and vertical directions can be provided by the adaptive weighting matrix according to the gradient information, so that the curve diffuses along the directions of local feature tangents of the objects. In addition, information irrelevant to the image target can be filtered out by the adaptive weighting matrix, thus reducing the interference of complex background. The alternating direction method of multipliers (ADMMs) is employed to solve the convex optimization problem in the first stage. In the second stage, the smoothed image obtained in the first stage is segmented by the deep learning method. By comparing with some traditional methods and deep learning methods, the results demonstrate that not only has good perceptual quality been achieved by this segmentation method, but also superior evaluation metrics have been obtained.