Textured Image Demoireing via Signal Decomposition and Guided Filtering
Textured Image Demoireing via Signal Decomposition and Guided Filtering
复制标题
通过信号分解和引导过滤实现纹理图像
DOI：
10.1109/tip.2017.2698920
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发表时间：
2017-07-01
影响因子：
10.6
通讯作者：
Wu, Feng
中科院分区：
文献类型：
--
作者：
Yang, Jingyu;Liu, Fanglei;Wu, Feng
莫尔伪影通常是由传感器采样网格与高频(近)周期性纹理的重叠造成的，严重影响图像质量。然而，由于莫尔图案的结构在某种意义上与纹理相似，因此很难有效地去除纹理图像中的莫尔伪影。本文提出了一种新的基于信号分解和导引滤波的纹理图像去噪方法。给出一幅含有莫尔伪影的纹理图像，我们首先使用所提出的低阶稀疏矩阵分解模型去除绿色(G)通道中的莫尔伪影。该模型在空间域利用低阶先验对纹理层进行正则化，在频域采用稀疏表示法对莫尔层进行正则化。采用增广拉格朗日乘子框架下的交替方向法求解矩阵分解模型。然后，由于红色(R)和蓝色(B)通道比G通道更容易受到莫尔伪影的污染，我们提出了通过获得G通道的纹理层进行引导滤波来去除R和B通道中的莫尔伪影。实验结果表明，对于合成图像和真实图像，我们的方法都优于目前最先进的方法。
Moire artifacts are generally caused by the interference between the overlap of the sensor's sampling grid and high-frequency (nearly) periodic textures, and heavily affect the image quality. However, it is difficult to effectively remove moire artifacts from textured images as the structure of moire patterns is similar to that of textures in some sense. In this paper, we propose a novel textured image demoireing method by signal decomposition and guided filtering. Given a textured image with moire artifacts, we first remove moire artifacts in the green (G) channel using the proposed low-rank and sparse matrix decomposition model. This model regularizes the texture layer by the low-rank prior in spatial domain and the moire layer by sparse representation in frequency domain. An alternating direction method under the augmented Lagrangian multiplier framework is used to solve the matrix decomposition model. Then, since the red (R) and blue (B) channels are more heavily polluted by moire artifacts than the G channel, we propose to remove moire artifacts in its R and B channels via guided filtering by the obtained texture layer of the G channel. Experimental results demonstrate that our method outperforms the state-of-the-art methods for both synthetic and real images.