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PDE-based image restoration: a hybrid model and color image denoising

机译:基于PDE的图像恢复:混合模型和彩色图像去噪

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The paper is concerned with PDE-based image restoration. A new model is introduced by hybridizing a nonconvex variant of the total variation minimization (TVM) and the motion by mean curvature (MMC) in order to deal with the mixture of the impulse and Gaussian noises reliably. We suggest the essentially nondissipative (ENoD) difference schemes for the MMC component to eliminate the impulse noise with a minimum (ideally no) introduction of dissipation. The MMC-TVM hybrid model and the ENoD schemes are applied for both gray-scale and color images. For color image denoising, we consider the chromaticity-brightness decomposition with the chromaticity formulated in the angle domain. An incomplete Crank-Nicolson alternating direction implicit time-stepping procedure is adopted to solve those differential equations efficiently. Numerical experiments have shown that the new hybrid model and the numerical schemes can remove the mixture of the impulse and Gaussian noises, efficiently and reliably, preserving edges quite satisfactorily.
机译:本文涉及基于PDE的图像恢复。通过将总变化量最小化(TVM)的非凸变体与平均曲率运动(MMC)混合,从而引入新模型,以便可靠地处理脉冲噪声和高斯噪声的混合。我们建议使用MMC组件的基本非耗散(ENoD)差分方案,以最小程度地(理想情况下为否)引入耗散来消除脉冲噪声。 MMC-TVM混合模型和ENoD方案适用于灰度和彩色图像。对于彩色图像去噪,我们考虑色度-亮度分解,其中色度在角域中表示。采用不完全的Crank-Nicolson交变方向隐式时间步长法有效地求解了这些微分方程。数值实验表明,新的混合模型和数值方案可以有效,可靠地消除脉冲噪声和高斯噪声的混合,并保持令人满意的边缘。

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