FHDe²Net: Full High Definition Demoireing Network

Abstract

Frequency aliasing in the digital capture of display screens leads to the moir´e pattern, appearing as stripe-shaped distortions in images. Efforts to demoir´eing have been made recently in a learning fashion due to the complexity and diversity of the pattern appearance. However, existing methods cannot satisfy the practical demand of demoir´eing on camera phone capturing more pixels than a full high definition (FHD) image, which poses additional challenges of wider pattern scale range and fine detail preservation. We propose the Full High Definition Demoir´eing Network (FHDe$^2$Net) to solve such problems. The framework consists of a global to local cascaded removal branch to eradicate multi-scale moir´e patterns and a frequency based high resolution content separation branch to retain fine details. We further collect an FHD moir´e image dataset as a new benchmark for training and evaluation. Comparison experiments and ablation studies have verified the effectiveness of the proposed framework and each functional module both quantitatively and qualitatively in practical application scenarios.