Efficient High-Fidelity Global Low-Rank Optimization for Multispectral Demosaicing

The nonlocal low-rank (NLR) optimization has shown promise for generalized multispectral filter array (MSFA) demosaicing. However, it faces challenges in balancing efficiency and accuracy. To tackle these challenges, we report here the multi-channel global low-rank optimization technique, achieving efficient high-fidelity MSFA demosaicing. Inspired by the cross-band correlations of natural multispectral images, we introduce the multi-channel matching and low-rank strategies that jointly optimize image patches of all channels, exhibiting higher efficiency and accuracy than existing approaches. Furthermore, we present global structural matching (GSM) which performs structure-aware multi-channel matching across the entire multispectral image. GSM extracts structurally important patches and efficiently searches their similar patches via parallel correlation, providing an order-of-magnitude improvement in efficiency. By combining the aforementioned techniques, we have achieved superior performance over the state-of-the-art NLR demosaicing technique, leading to up to 3.9 dB peak signal-to-noise ratio (PSNR) gain and over a 150-fold increase in computational speed. Experiments validated that the technique outperforms existing methods in reconstructing fine textures and details and exhibits superior robustness to noise.