Moiré pattern removal from texture images via low-rank and sparse matrix decomposition

Abstract

Moiré patterns, an artifact of aliasing interference between details in the subject matter and the grid of the sensor, heavily disturb the qualitative and quantitative analysis of images. It is hard to effectively remove moiré patterns since they are similar to image textures. We propose a novel low-rank and sparse matrix decomposition model for moiré pattern removal. This method is grounded on the observation: textures are locally well-patterned while moiré patterns are dissimilar, and the energy distribution of moiré patterns in the frequency domain is concentrated and almost no mixed with that of textures. For each patch, texture component is regularized by a low-rank prior and moiré component is regularized by a sparse prior in the discrete cosine transform (DCT) domain. This model is effectively solved by the alternating direction method under the augmented Lagrangian multiplier (ALM-ADM) algorithm. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods.