Spatial-Frequency Fusion for Bayer Demosaicking

Chenyan Bai, Jia Li, Jinbiao Wang

Abstract: Deep learning-based demosaicking for the Bayer color filter array (CFA) has great advances. However, most existing demosaicking methods only work in the spatial domain and rarely explore the spectral characteristic of Bayer CFA. In this letter, we first attempt to address Bayer demosaicking in both spatial and frequency domains and propose a spatial-frequency fusion network. It consists of three key modules: spatial-domain branch, frequency-domain branch, and dual-domain fusion. Spatial-domain branch employs the standard convolution to extract local features in the spatial domain, while frequency-domain branch adopts frequency selection to maintain CFA's periodicity and achieve the image-wide receptive field for obtaining global features. Dual-domain fusion integrates the complementary representation of the two types of features. Extensive experiments validate the effectiveness of the proposed network.