Multiplicative noise removal via sparse and redundant representations over learned dictionaries and total variation
Multiplicative noise removal via sparse and redundant representations over learned dictionaries and total variation
复制标题
DOI：
10.1016/j.sigpro.2011.12.015
复制
发表时间：
2012-06
期刊：
影响因子：
--
通讯作者：
Yan Hao;Xiangchu Feng;Jianlou Xu
中科院分区：
文献类型：
--
作者：
Yan Hao;Xiangchu Feng;Jianlou Xu
本文提出了一种新的三阶段乘性噪声去除模型。在第一阶段，使用稀疏和冗余表示来逼近原木图像。采用K-SVD算法训练能够描述原木图像稀疏性的冗余字典。然后在第二阶段，我们使用总变分(TV)方法对得到的图像进行修正。最后，通过指数函数和偏差修正，将结果从对数域转换回实数域。我们的方法结合了稀疏和冗余表示的优点，优于训练词典和TV方法。实验结果表明，新模型比现有模型更能有效地滤除乘性噪声。
In this paper, we propose a new three-stage model for multiplicative noise removal. In the first stage, sparse and redundant representation is used to approximate the log-image. The K-SVD algorithm is used to train a redundant dictionary, which can describe the log-image sparsity. Then in the second stage, we use the total variation (TV) method to amend the image obtained. At last, via an exponential function and bias correction, the result is transformed back from the log-domain to the real one. Our method combines the advantages of sparse and redundant representation over trained dictionary and TV method. Experimental results show that the new model is more effective to filter out multiplicative noise than the state-of-the-art models.