Data-driven Prior for Pharmaceutical Snapshot Spectral Imaging

This paper proposes a new method for pharmaceutical hyperspectral compressive imaging and has a significant improvement for the quality of reconstruction. It's known that coded aperture snapshot spectral imager(CASSI) overcomes the limitation of hyperspectral image acquisition. However, the spatial and spectral information is coded and overlapped which make it difficult to reconstruct the original images. The reconstruction is an inverse mathematical problem which is barely solved precisely especially in complex imaging scenes such as irregular pharmaceutical product imaging. Thus, we consider the real pharmaceutical imaging demands and propose a novel image restoration method with the data-driven prior. Our method is based on the generalized alternating projection(GAP) framework and propose a novel denoising part to solve the problem of detail texture feature extraction with the dense block module employed. Our method is tested on real pharmaceutical hyperspectral data and achieve higher performance compared with state of the art methods.