IEEE Trans Image Process. 2017 Jul;26(7):3528-3541. doi: 10.1109/TIP.2017.2698920. Epub 2017 Apr 27.
Moiré 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 moiré artifacts from textured images as the structure of moiré patterns is similar to that of textures in some sense. In this paper, we propose a novel textured image demoiréing method by signal decomposition and guided filtering. Given a textured image with moiré artifacts, we first remove moiré 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 moiré 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 moiré artifacts than the G channel, we propose to remove moiré 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.
摩尔纹伪影通常是由于传感器的采样网格与高频（近）周期性纹理的重叠干扰引起的，严重影响图像质量。然而，由于摩尔纹图案的结构在某种意义上与纹理的结构相似，因此很难有效地从具有纹理的图像中去除摩尔纹伪影。在本文中，我们提出了一种基于信号分解和导向滤波的新的纹理图像去摩尔纹方法。对于具有摩尔纹伪影的纹理图像，我们首先使用提出的低秩稀疏矩阵分解模型去除 G 通道中的摩尔纹伪影。该模型通过空间域的低秩先验和频率域的稀疏表示来正则化纹理层和摩尔纹层。利用增广拉格朗日乘子框架下的交替方向法来求解矩阵分解模型。然后，由于 R 通道和 B 通道比 G 通道受到摩尔纹伪影的污染更严重，我们提出通过对 G 通道获得的纹理层进行导向滤波来去除 R 通道和 B 通道中的摩尔纹伪影。实验结果表明，我们的方法在合成和真实图像上均优于最新的方法。
