A First Derivative Potts Model for Segmentation and Denoising Using MILP
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Unsupervised image segmentation and denoising are two fundamental tasks in image processing. Usually, graph based models such as multicut are used for segmentation and variational models are employed for denoising. Our approach addresses both problems at the same time. We propose a novel MILP formulation of a first derivative Potts model, where binary variables are introduced to directly deal with the ℓ_0 norm. As a by-product the image is denoised. To the best of our knowledge, it is the first global mathematical programming model for simultaneous segmentation and denoising. Numerical experiments on real-world images are compared with multicut approaches.
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