Abstract
Multispectral filter array (MSFA)-based imaging is a compact, practical technique for snapshot spectral image capturing and reconstruction. The imaging and reconstruction quality is highly influenced by the spectral sensitivities and spatial arrangement of channels on MSFAs, and the used reconstruction method. In order to design a MSFA with high imaging capacity, we propose a sparse representation based approach to optimize spectral sensitivities and spatial arrangement of MSFAs. The proposed approach first overall models the various errors associated with spectral reconstruction, and then uses a global heuristic searching method to optimize MSFAs via minimizing the estimated error of MSFAs. Our MSFA optimization method can select filters from off-the-shelf candidate filter sets while assigning the selected filters to the designed MSFA. Experimental results on three datasets show that the proposed method is more efficient, flexible, and can design MSFAs with lower spectral construction errors when compared with existing state-of-the-art methods. The MSFAs designed by our method show better performance than others even using different spectral reconstruction methods.
Highlights
Multi-spectral images are widely used in many applications such as material classification, object recognition, and color constancy
Most of the existing Multispectral filter array (MSFA) are usually designed in an ad-hoc manner, e.g., the spectral sensitivity function of channels are designed as a series of band-pass functions with a close full width at half maximum (FWHM) and the same intervals
We firstly introduced a sparse coding method to directly recover multispectral images from raw outputs of MSFA sensors; we estimate and qualify the reconstruction errors of the spectral reconstruction method to evaluate the capability of MSFA sensors; the optimal MSFA can be achieved by (1) selecting a subset from a large candidate set of commercial filters; (2) assigning the selected filters onto MSFA mosaic pattern that minimizes the estimated reconstruction errors using a heuristic global searching method
Summary
Multi-spectral images are widely used in many applications such as material classification, object recognition, and color constancy. Previous works [4,5,6,7] optimize either (a) the spectral sensitivity functions of channels or (b) the arrangement of channels on periodic mosaic pattern These methods treat the two issues separately cannot guarantee the optimal MSFA design. We firstly introduced a sparse coding method to directly recover multispectral images from raw outputs of MSFA sensors; we estimate and qualify the reconstruction errors of the spectral reconstruction method to evaluate the capability of MSFA sensors; the optimal MSFA can be achieved by (1) selecting a subset from a large candidate set of commercial filters; (2) assigning the selected filters onto MSFA mosaic pattern that minimizes the estimated reconstruction errors using a heuristic global searching method
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