Image Demoiréing with a Dual-Domain Distilling Network

Hailing Wang, Qiaoyu Tian, Liang Li, Xiaojie Guo

Abstract: Due to slight discrepancy of spatial frequency between the camera sensor array and sub-pixel layout of LCD monitor, moiré pattern artifacts appear in various shapes and colours which seriously degrade the quality of captured images. It is challenging yet practically crucial to remove moiré artifacts from a single camera-captured screen image. In this paper, we propose a dual-domain distilling network (3DNet for short) to tackle this problem in an end-to-end manner. The 3DNet consists of a dual-branch student network (a.k.a. demoiréing network), and two teacher networks. The two branches of student network exploit knowledge in both spatial-domain and frequency-domain for the sake of removing moiré artifacts, based on the observation that rich image details can be discovered in the frequency-domain while structure information can be well kept in the spatial-domain. The demoiréing process of two branches is supervised by the knowledge distilled from two teacher networks trained for reconstructing clear images in the spatial and frequency domains respectively. Comprehensive experimental results are conducted to demonstrate the efficacy of our design, and reveal its superiority over state-of-the-art alternatives.