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A capturing system with multispectral filter array (MSFA) technology is proposed for shortening the capture time and reducing costs. Therein, a mosaicked image captured using an MSFA is demosaicked to reconstruct multispectral images (MSIs). Joint optimization of the spectral sensitivity of the MSFAs and demosaicking is considered, and pathology-specific multispectral imaging is proposed. This optimizes the MSFA and the demosaicking matrix by minimizing the reconstruction error between the training data of a hematoxylin and eosin-stained pathological tissue and a demosaicked MSI using a cost function. Initially, the spectral sensitivity of the filter array is set randomly and the mosaicked image is obtained from the training data. Subsequently, a reconstructed image is obtained using Wiener estimation. To minimize the reconstruction error, the spectral sensitivity of the filter array and the Wiener estimation matrix are optimized iteratively through an interior-point approach. The effectiveness of the proposed MSFA and demosaicking is demonstrated by comparing the recovered spectrum and RGB image with those obtained using a conventional method.

A capturing system with multispectral filter array (MSFA) technology has been researched to shorten the capturing time and reduce the cost. In this system, the mosaicked image captured by the MSFA is demosaicked to reconstruct multispectral images (MSIs). We focus on the spectral sensitivity design of a MSFA in this paper and propose a pathology-specific MSFA. The proposed method optimizes the MSFA by minimizing the reconstruction error between training data of a pathological tissue and a demosaicked MSI under a cost function. Firstly, the spectral sensitivities of the filter array are set randomly, and the mosaicked image is obtained from the training data and the filter array. Then, a reconstructed image is obtained by Wiener estimation. The spectral sensitivities of the filter array are optimized iteratively by an interior-point approach to minimize the reconstruction error. We show the effectiveness of the proposed MSFA by comparing the recovered spectrum and RGB image with a conventional method.