IEEE Trans Image Process - Robust scale-space filter using second-order partial differential equations.

Tópicos

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Resumo

This paper describes a robust scale-space filter that adaptively changes the amount of flux according to the local topology of the neighborhood. In a manner similar to modeling heat or temperature flow in physics, the robust scale-space filter is derived by coupling Fick's law with a generalized continuity equation in which the source or sink is modeled via a specific heat capacity. The filter plays an essential part in two aspects. First, an evolution step size is adaptively scaled according to the local structure, enabling the proposed filter to be numerically stable. Second, the influence of outliers is reduced by adaptively compensating for the incoming flux. We show that classical diffusion methods represent special cases of the proposed filter. By analyzing the stability condition of the proposed filter, we also verify that its evolution step size in an explicit scheme is larger than that of the diffusion methods. The proposed filter also satisfies the maximum principle in the same manner as the diffusion. Our experimental results show that the proposed filter is less sensitive to the evolution step size, as well as more robust to various outliers, such as Gaussian noise, impulsive noise, or a combination of the two.

Resumo Limpo

paper describ robust scalespac filter adapt chang amount flux accord local topolog neighborhood manner similar model heat temperatur flow physic robust scalespac filter deriv coupl fick law general continu equat sourc sink model via specif heat capac filter play essenti part two aspect first evolut step size adapt scale accord local structur enabl propos filter numer stabl second influenc outlier reduc adapt compens incom flux show classic diffus method repres special case propos filter analyz stabil condit propos filter also verifi evolut step size explicit scheme larger diffus method propos filter also satisfi maximum principl manner diffus experiment result show propos filter less sensit evolut step size well robust various outlier gaussian nois impuls nois combin two

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