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A Dual Formulation of the TV-Stokes Algorithm for Image Denoising

机译:电视斯托克斯算法的图像去噪的双重表示

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We propose a fast algorithm for image denoising, which is based on a dual formulation of a recent denoising model involving the total variation minimization of the tangential vector field under the in-compressibility condition stating that the tangential vector field should be divergence free. The model turns noisy images into smooth and visually pleasant ones and preserves the edges quite well. While the original TV-Stokes algorithm, based on the primal formulation, is extremely slow, our new dual algorithm drastically improves the computational speed and possesses the same quality of denoising. Numerical experiments are provided to demonstrate practical efficiency of our algorithm.
机译:我们提出了一种快速的图像去噪算法,该算法基于最近的降噪模型的双重表述,该模型涉及在不可压缩条件下切向矢量场的总变化最小化,指出切向矢量场应无散度。该模型将嘈杂的图像转换为平滑且视觉上令人愉悦的图像,并很好地保留了边缘。虽然原始的基于原始公式的TV-Stokes算法非常慢,但我们的新对偶算法极大地提高了计算速度,并具有相同的降噪质量。提供数值实验以证明我们算法的实际效率。

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