Multispectral Image Demosaicking Based on Multi-scale Dense Connections and Large-kernel Attention

Single-sensor multispectral cameras generally employ a multispectral filter array (MSFA) to rapidly acquire spatial-spectral information. However, MSFA cameras capture only one spectral band’s information at each pixel location. Thus, to fully utilize the spatial-spectral information recorded by MSFA cameras, demosaicing methods are necessary. In this paper, we propose a novel two-stage demosaicing method that combines the traditional method and the deep learning method. We first use a weighted bilinear interpolation convolution filter to preliminarily interpolate the raw MSFA image. And then a multi-scale dense connections large kernel attention method (MDLKA) is proposed to further reconstruct the full-resolution spectral images. MDLKA can effectively increase the image receptive field and capture long-range dependence. Through dense connections, attention maps of different scales are fused to enrich image features. Finally, compared with current advanced demosaicing methods on the dataset provided by NTIRE 2022, we exceed traditional interpolation methods by 11–15 dB in PSNR. Compared with the mosaic convolution-attention network (MCAN) and Res2Unet network based on deep learning methods, we exceed them by 5.4 dB and 1.95 dB in PSNR, respectively.