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首页> 外文期刊>Circuits, systems, and signal processing >A Multi-parameter Regularization Model for Deblurring Images Corrupted by Impulsive Noise
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A Multi-parameter Regularization Model for Deblurring Images Corrupted by Impulsive Noise

机译：一种多参数正则化模型，对脉冲噪声破坏的图像进行模糊处理

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摘要

This article considers image deblurring in the presence of impulse noise and proposes a new multi-parameter regularization model for image deblurring based on total variation (TV) and wavelet frame (WF), along with an efficient and effective solving algorithm for this restoring model. On the one hand, it is well known that the TV regularization-based Rudin-Osher-Fatemi model is very effective in preserving sharp edges and object boundaries which are generally the most important features to recover. On the other hand, WF-based approaches for image restoration have proven to be very successful in adaptively exploiting the regularity of natural images. By combining TV regularization and WF regularization, a novel multi-parameter regularization model is proposed for deblurring images in the presence of impulse noise. Numerically, the alternative direction method of multiplier (ADMM) with an adaptive scheme for choosing regularization parameters is provided and applied to this multi-parameter regularization model. Moreover, the convergence analysis of the ADMM is shown in the "Appendix." Furthermore, numerical experiments involving images corrupted by various types of blurring kernels and different levels of noises indicate that the proposed model and algorithm outperform several state-of-the-art approaches in terms of the restoration quality, especially the ability to mitigate staircasing effects while preserving important features in images.
机译：本文考虑了存在脉冲噪声时的图像去模糊，并提出了一种基于总变化量（TV）和小波帧（WF）的图像去模糊多参数正则化模型，以及针对该恢复模型的高效求解算法。一方面，众所周知，基于电视正则化的Rudin-Osher-Fatemi模型在保留通常是最重要的恢复特征的锐利边缘和对象边界方面非常有效。另一方面，事实证明，基于WF的图像恢复方法在自适应利用自然图像的规律性方面非常成功。通过结合TV正则化和WF正则化，提出了一种新型的多参数正则化模型，用于在脉冲噪声存在下对图像进行去模糊处理。在数值上，提供了带有自适应规则选择正则化参数的乘数的交替方向法（ADMM），并将其应用于该多参数正则化模型。此外，“附录”中显示了ADMM的收敛性分析。此外，涉及由各种类型的模糊核和不同级别的噪声破坏的图像的数值实验表明，在恢复质量方面，所提出的模型和算法优于几种最新方法，尤其是在减轻楼梯效应的同时保留图像中的重要特征。

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