Quad Bayer Joint Demosaicing and Denoising Based on Dual Encoder Network with Joint Residual Learning

The recent imaging technology Quad Bayer color filter array (CFA) brings great imaging performance improvement from traditional Bayer CFA, but also serious challenges for demosaicing and denoising during the image signal processing (ISP) pipeline. In this paper, we propose a novel dual encoder network, namely DRNet, to achieve joint demosaicing and denoising for Quad Bayer CFA. The dual encoders are carefully designed in that one is mainly constructed by a joint residual block to jointly estimate the residuals for demosaicing and denoising separately. In contrast, the other one is started with a pixel modulation block which is specially designed to match the characteristics of Quad Bayer pattern for better feature extraction. We demonstrate the effectiveness of each proposed component through detailed ablation investigations. The comparison results on public benchmarks illustrate that our DRNet achieves an apparent performance gain (0.38dB to the second best) from the state-of-the-arts and balances performance and efficiency well. The experiments on real-world images show that the proposed method could enhance the reconstruction quality from the native ISP algorithm.