Enhancing multispectral image demosaicking from MSFA sensors using a triangular structure-aware bilinear interpolation: Application to one-shot and multi-shot systems

Multispectral imaging enables detailed material analysis by capturing multiple spectral bands. Conventional systems acquire each band at full resolution but are costly and slow. Compact one-shot multispectral filter array (MSFA) sensors offer a practical alternative, capturing subsampled spectral mosaics that require demosaicking to recover full-resolution images. However, traditional spatial interpolation methods such as Weighted Bilinear (WB) and BTES often introduce blurring or directional artifacts in regions with strong spatial variation. To address these limitations, we introduce Triangular Structure-aware Bilinear Interpolation (TriSBI), a geometry-driven spatial method designed to better preserve edges and textured structures in MSFA mosaics. While TriSBI improves over WB and BTES, the severe undersampling inherent to one-shot MSFA sensors still restricts any spatial method’s ability to reconstruct sharp transitions and fine details. Motivated by this constraint, we adopt a multi-shot acquisition strategy with controlled subpixel shifts, increasing the sampling density of each spectral band. This enhanced sampling strengthens spatial correlations and allows TriSBI to deliver sharper reconstructions in contour-rich and highly textured regions. Beyond its standalone performance, TriSBI also provides a stronger spatial foundation for spectral–spatial demosaicking. Its integration into Spectral Difference (SD) and Iterative Spectral Difference (ISD) improves inter-band correlation modeling and yields clearer reconstructions of complex structures compared to WB-based baselines. Experiments on the CAVE and TokyoTech datasets show consistent improvements in PSNR, SSIM, and SAM for both one-shot and multi-shot configurations, demonstrating the effectiveness of combining geometry-aware spatial interpolation with enhanced acquisition strategies for MSFA-based multispectral imaging. • Introduction of a geometry-aware spatial interpolation method (TriSBI) for improved MSFA demosaicking. • TriSBI reduces blurring and artifacts compared to conventional Weighted Bilinear and BTES methods. • A multi-shot acquisition strategy is employed to mitigate spectral undersampling, enhancing spatial reconstruction. • Integrating TriSBI into SD/ISD spectral-spatial methods, particularly with multi-shot, significantly boosts multispectral image quality. • Experiments on realistic sensor-based projections of CAVE and TokyoTech datasets confirm gains in PSNR, SSIM, and SAM.