The prevalence of digital sensors, such as digital cameras and mobile phones,
simplifies the acquisition of photos. Digital sensors, however, suffer from
producing Moire when photographing objects having complex textures, which
deteriorates the quality of photos. Moire spreads across various frequency
bands of images and is a dynamic texture with varying colors and shapes, which
pose two main challenges in demoireing---an important task in image
restoration. In this paper, towards addressing the first challenge, we design a
multi-scale network to process images at different spatial resolutions,
obtaining features in different frequency bands, and thus our method can
jointly remove moire in different frequency bands. Towards solving the second
challenge, we propose a dynamic feature encoding module (DFE), embedded in each
scale, for dynamic texture. Moire pattern can be eliminated more effectively
via DFE.Our proposed method, termed Multi-scale convolutional network with
Dynamic feature encoding for image DeMoireing (MDDM), can outperform the state
of the arts in fidelity as well as perceptual on benchmarks.

本文提出了一种基于多尺度卷积网络和动态特征编码的数字传感器图像去莫尔算法，可以同时处理不同频带的莫尔，且效果超过现有技术。