首页> 外文会议>Computational Intelligence and Design, 2009. ISCID '09 >Image Deblurring with Impulse Noise Using Split Bregman Algorithm
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Image Deblurring with Impulse Noise Using Split Bregman Algorithm

机译:使用分裂Bregman算法的脉冲噪声图像去模糊

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We propose an effective method to resolve blurred images with impulse noise. Our method has two steps. First, an improved adaptive median filter is proposed for image denoising; And second, the problem of deblurring the denoised image is formulated as to minimize the object function which consists of L1 data-fedility term and double regularization term. The minimization problem is solved by split Bregman algorithm. Numerical results using image with different blurs and impulse noise show that the proposed method gives better performance than the variable splitting alternative minimization algorithm in [10] by objective peak signal to noise ratio and subjective vision quality, which demonstrates the efficiency of our proposed algorithms.
机译:我们提出了一种有效的方法来解决带有脉冲噪声的模糊图像。我们的方法有两个步骤。首先,提出了一种改进的自适应中值滤波器,用于图像去噪。其次,将去噪图像去模糊化的问题提出为最小化由L1数据联邦项和双重正则项组成的目标函数。最小化问题通过分裂Bregman算法解决。使用具有不同模糊和脉冲噪声的图像的数值结果表明,该方法与客观峰值信噪比和主观视觉质量相比,在[10]中的可变分割替代性最小化算法具有更好的性能,从而证明了该算法的有效性。

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