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Detecting moir&#x00E9; patterns in digital photographs is meaningful as it provides priors towards image quality evaluation and demoir&#x00E9;ing tasks. In this paper, we present a simple yet efficient framework to extract moir&#x00E9; edge maps from images with moir&#x00E9; patterns. The framework includes a strategy for training triplet (natural image, moir&#x00E9; layer, and their synthetic mixture) generation, and a Moir&#x00E9; Pattern Detection Neural Network (MoireDet) for moir&#x00E9; edge map estimation. This strategy ensures consistent pixel-level alignments during training, accommodating characteristics of a diverse set of camera-captured screen images and real-world moir&#x00E9; patterns from natural images. The design of three encoders in MoireDet exploits both high-level contextual and low-level structural features of various moir&#x00E9; patterns. Through comprehensive experiments, we demonstrate the advantages of MoireDet: better identification precision of moir&#x00E9; images on two datasets, and a marked improvement over state-of-the-art demoir&#x00E9;ing methods.

Doing More with Moiré Pattern Detection in Digital Photos