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A restoration algorithm for images contaminated by mixed Gaussian plus random-valued impulse noise

机译:混合高斯加随机值脉冲噪声污染图像的恢复算法

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摘要

In this paper, we study the problem of restoring the image corrupted by additive Gaussian noise plus random-valued impulse noise. A novel noise classifier is firstly created to identify different noise in the corrupted image. Then, we use the remaining effective information to train an adaptive overcomplete dictionary for sparse representation of image patches with the help of masked K-SVD algorithm. Because of the adaptive nature of the learned dictionary, it can represent the image patches in concern more efficiently. Then, we minimize a variational model containing an optional data-fidelity term and a smooth regularization term respecting sparse representation of every image patch to get the final restored image. Extensive experimental results prove that our method cannot only remove noise from the corrupted image well, but also preserve more details and textures. It surpasses some state-of-the-art methods.
机译:在本文中,我们研究恢复由加性高斯噪声加随机值脉冲噪声破坏的图像的问题。首先创建一种新颖的噪声分类器,以识别损坏图像中的不同噪声。然后,我们利用剩余的有效信息,借助蒙版K-SVD算法,训练了一个自适应的超完备字典,用于稀疏表示图像块。由于学习词典的自适应特性,它可以更有效地表示所关注的图像块。然后,我们最小化一个变体模型,该模型包含一个可选的数据保真度项和一个平滑的正则化项,以尊重每个图像补丁的稀疏表示,以获得最终的还原图像。大量的实验结果证明,我们的方法不仅可以很好地消除图像中的噪点,而且可以保留更多的细节和纹理。它超越了一些最先进的方法。

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