Based on the problems of high noise, lower contrast, and complex features in defaced images and the low accuracy of existing defaced image segmentation techniques, this paper proposes a defaced image segmentation algorithm based on DAC-CLGD-Danet. Firstly, a CBDNet asymmetric blind denoising network is used for noise-containing defaced images, and natural and synthetic images are trained together to model the image noise and enhance the denoising ability of natural noise. Secondly, Danet is used as the base network. A Dense Atrous Convolution module (DAC) is added to the dual attention mechanism module to extend the perceptual domain of deep convolution, reduce image feature loss, and enhance the representation of global information and edge features of defaced images; Cross-Level Gating Decoder module (CLGD) is introduced to lighten the segmentation network, enhance image context aggregation, and produce accurate semantic segmentation. The experimental results demonstrated that the method in this paper has a significant effect on the HRF dataset and Cityscapes dataset, with a significant improvement compared with FCN, UNet, and SETR models, with Intersection over Union (IoU) improved by 9.81% and Mean Intersection over Union (mIoU) improved by 3.01% compared with UNet.

Level-set-based image segmentation has been widely used in unsupervised segmentation tasks. Researchers have recently alleviated the influence of image noise on segmentation results by introducing global or local statistics into existing models. Most existing methods are based on the assumption that the distribution of image noise is known or observable. However, real-time images do not meet this assumption. To bridge this gap, we propose a novel level-set-based segmentation method with an unsupervised denoising mechanism. First, a denoising filter is acquired under the unsupervised learning paradigm. Second, the denoising filter is integrated into the level-set framework to separate noise from the noisy image input. Finally, the level-set energy function is minimized to acquire segmentation contours. Extensive experiments demonstrate the robustness and effectiveness of the proposed method when applied to noisy images.

This paper proposes a parameter adaptive hybrid model for image segmentation. The hybrid model combines the global and local information in an image, and provides an automated solution for adjusting the selection of the two weight parameters. Firstly, it combines an improved local model with the global Chan-Vese (CV) model , while the image’s local entropy is used to establish the index for measuring the image’s gray-level information. Parameter adjustment is then performed by the real-time acquisition of the ratio of the different functional energy in a self-adapting model responsive to gray-scale distribution in the image segmentation process. Compared with the traditional linear adjustment model, which is based on trial-and-error, this paper presents a more quantitative and intelligent method for achieving the dynamic nonlinear adjustment of global and local terms. Experiments show that the proposed model achieves fast and accurate segmentation for different types of noisy and non-uniform grayscale images and noise images. Moreover, the method demonstrates high stability and is insensitive to the position of the initial contour.

It remains a challenging task to segment images that are distorted by noise and intensity inhomogeneity. To overcome these problems, in this paper, we present a novel region-based active contour model based on local intensity information and a kernel metric. By introducing intensity information about the local region, the proposed model can accurately segment images with intensity inhomogeneity. To enhance the model’s robustness to noise and outliers, we introduce a kernel metric as its objective functional. To more accurately detect boundaries, we apply convex optimization to this new model, which uses a weighted total-variation norm given by an edge indicator function. Lastly, we use the split Bregman iteration method to obtain the numerical solution. We conducted an extensive series of experiments on both synthetic and real images to evaluate our proposed method, and the results demonstrate significant improvements in terms of efficiency and accuracy, compared with the performance of currently popular methods.