Adjustable spatial resolution of compressive spectral images sensed by multispectral filter array-based sensors

Abstract

Spectral imaging systems capture spectral and spatial information from a scene to produce a spectral data cube. Technical progress has allowed developing multispectral filter array (MSFA)-based sensors in order to expand the reconstruction of more bands than RGB cameras. However, reconstructing the spectral image with traditional methods following a least squares or demosaicing approach is unfeasible. Some works in the literature implement multispectral demosaicing for reconstructing images with specific spatio-spectral resolution depending on the number of pixels in the detector and the filter mosaic. Recently, compressive sensing technique has been developed that allows reconstructing signals with fewer measurements than the traditional methods by using the sparse representation of a signal. The selection of neighborhoods pixels in the MSFA-based sensor to calculate the spectral response of a single pixel in the reconstructed spectral images could improve the reconstruction, based on exploiting the sparse representation of the spectral images. This paper proposes two models for spectral images reconstruction from the selection of MSFA-based sensor measurements neighborhoods using the principle of compressive sensing. The spatial resolution of the reconstructed spectral images is adjusted depending the size of the neighborhood. To verify the effectiveness of the reconstruction models simulated measurements for synthetic spectral images and real spectral images based on MSFA are used. Ensembles of random dichroic and random band pass filters are used. The two approaches with traditional scheme reconstructions of mosaic filters are compared. The proposed methods improve the quality (PSNR) of the image reconstruction up 7 dB for real spectral images.