Data-driven Prior for Pharmaceutical Snapshot Spectral Imaging

Xuesan Su, Jianxu Mao, Yaonan Wang, Yurong Chen, Hui Zhang
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Abstract

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.
药物快照光谱成像的数据驱动先验
本文提出了一种新的药物高光谱压缩成像方法,对图像的重建质量有了明显的提高。编码孔径快照光谱成像仪克服了高光谱图像采集的局限性。然而,由于空间和光谱信息被编码和重叠,使得原始图像难以重建。特别是在不规则药品成像等复杂成像场景中,图像重建是一个难以精确解决的逆数学问题。因此,我们从实际的药物成像需求出发,提出了一种基于数据驱动先验的图像恢复方法。该方法基于广义交替投影(GAP)框架,提出了一种新的去噪部分,利用密集块模块来解决细节纹理特征提取问题。我们的方法在真实的药物高光谱数据上进行了测试,与最先进的方法相比,取得了更高的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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