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Image segmentation and image restoration are two important topics in imageprocessing with great achievements. In this paper, we propose a new multiphasesegmentation model by combining image restoration and image segmentationmodels. Utilizing image restoration aspects, the proposed segmentation modelcan effectively and robustly tackle high noisy images, blurry images, imageswith missing pixels, and vector-valued images. In particular, one of the mostimportant segmentation models, the piecewise constant Mumford-Shah model, canbe extended easily in this way to segment gray and vector-valued imagescorrupted for example by noise, blur or missing pixels after coupling a newdata fidelity term which comes from image restoration topics. It can be solvedefficiently using the alternating minimization algorithm, and we prove theconvergence of this algorithm with three variables under mild condition.Experiments on many synthetic and real-world images demonstrate that our methodgives better segmentation results in comparison to others state-of-the-artsegmentation models especially for blurry images and images with missing pixelsvalues.

Variational Image Segmentation Model Coupled with Image Restoration Achievements