Segmentation of noisy images having light in the background it is a challenging task for the existing segmentation approaches and methods. In this paper, we suggest a novel variational method for joint restoration and segmentation of noisy images which are having intensity and inhomogeneity in the existence of high contrast light in the background. The proposed model combines statistical local region information of circular regions centered at each pixel with a multi-phase segmentation technique enabling inhomogeneous image restoration. The proposed model is written in the fuzzy set framework and resolved through alternating direction minimization approach of multipliers. Through experiments, we have tested the performance of the suggested approach on diverse types of synthetic and real images in the existence of intensity and in-homogeneity; and evaluate the precision, as well as, the robustness of the suggested model. Furthermore, the outcomes are, then, compared with other state-of-the-art models including two-phase and multi-phase approaches and show that our method has superiority for images in the existence of noise and inhomogeneity. Our empirical evaluation and experiments, using real images, evaluate and assess the efficiency of the suggested model against several other closest rivals. We observed that the suggested model can precisely segment all the images having brightness, diffuse edges, high contrast light in the background, and inhomogeneity.
Segmentation of noisy images having light in the background it is a challenging task for the existing segmen-tation models. In this paper, we propose a new variational model for joint restoration and segmentation of noisy images having intensity inhomogeneity in the presence of high contrast light in the background. The proposed model combines statistical local region information of circular regions centered at each pixel with a multi-phase segmentation technique enabling inhomogeneous image restoration. The proposed model is written in the fuzzy set framework and solved by alternating direction minimization method of multipliers. Through experiments, we have tested our proposed model on different types of synthetic and real images in the presence of intensity in-homogeneity and demonstrate the accuracy and the robustness of the proposed model. In addition, the results are compared with state of the art two-phase and multi-phase methods and show that our method has superiority for images in the presence of noise and inhomogeneity.
Segmenting natural and outdoor images are challenging for most of the latest variational segmentation models. For this purpose we employ derived image data (DID) and propose a robust variational model. The DID rely on three images by utilizing image local and global statistics as well as filter image which is obtained through our design high pass filtering techniques. Then these derived image data are incorporated into our proposed energy functional which can robustly segment images having inhomogeneity, mix backgrounds and multi-regions. Furthermore, the results of DID are compared with other well known methods with finding Jaccard similarity index to proof the efficient and qualitative performance of proposed model over the traditional methods. Finally, the proposed
DID based model is tested on real world 3D images to ensure that it also
preserve its performance in vector valued images as well.
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