IEEE Trans Image Process - Efficient algorithm for nonconvex minimization and its application to PM regularization.

Tópicos

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Resumo

In image processing, nonconvex regularization has the ability to smooth homogeneous regions and sharpen edges but leads to challenging computation. We propose some iterative schemes to minimize the energy function with nonconvex edge-preserving potential. The schemes are derived from the duality-based algorithm proposed by Berm?dez and Moreno and the fixed point iteration. The convergence is proved for the convex energy function with nonconvex potential and the linear convergence rate is given. Applying the proposed schemes to Perona and Malik's nonconvex regularization, we present some efficient algorithms based on our schemes, and show the approximate convergence behavior for nonconvex energy function. Experimental results are presented, which show the efficiency of our algorithms, including better denoised performance of nonconvex regularization, faster convergence speed, higher calculation precision, lower calculation cost under the same number of iterations, and less implementation time under the same peak signal noise ratio level.

Resumo Limpo

imag process nonconvex regular abil smooth homogen region sharpen edg lead challeng comput propos iter scheme minim energi function nonconvex edgepreserv potenti scheme deriv dualitybas algorithm propos bermdez moreno fix point iter converg prove convex energi function nonconvex potenti linear converg rate given appli propos scheme perona malik nonconvex regular present effici algorithm base scheme show approxim converg behavior nonconvex energi function experiment result present show effici algorithm includ better denois perform nonconvex regular faster converg speed higher calcul precis lower calcul cost number iter less implement time peak signal nois ratio level

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