编码孔径
高光谱成像
计算机科学
迭代重建
人工智能
计算机视觉
快照(计算机存储)
光谱成像
卷积神经网络
模式识别(心理学)
全光谱成像
图像质量
压缩传感
图像(数学)
光学
探测器
操作系统
物理
电信
作者
Lizhi Wang,Tao Zhang,Ying Fu,Hua Huang
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:2019-05-01
卷期号:28 (5): 2257-2270
被引量:117
标识
DOI:10.1109/tip.2018.2884076
摘要
Coded aperture snapshot spectral imaging (CASSI) system encodes the 3D hyperspectral image (HSI) within a single 2D compressive image and then reconstructs the underlying HSI by employing an inverse optimization algorithm, which equips with the distinct advantage of snapshot but usually results in low reconstruction accuracy. To improve the accuracy, existing methods attempt to design either alternative coded apertures or advanced reconstruction methods, but cannot connect these two aspects via a unified framework, which limits the accuracy improvement. In this paper, we propose a convolution neural network-based end-to-end method to boost the accuracy by jointly optimizing the coded aperture and the reconstruction method. On the one hand, based on the nature of CASSI forward model, we design a repeated pattern for the coded aperture, whose entities are learned by acting as the network weights. On the other hand, we conduct the reconstruction through simultaneously exploiting intrinsic properties within HSI-the extensive correlations across the spatial and spectral dimensions. By leveraging the power of deep learning, the coded aperture design and the image reconstruction are connected and optimized via a unified framework. Experimental results show that our method outperforms the state-of-the-art methods under both comprehensive quantitative metrics and perceptive quality.
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