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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Compressive Hyperspectral Imaging and Super-resolution

DOI:10.1109/ICIVC.2018.8492822 出版年份:2018 更新时间:2025-09-23 15:22:29
摘要: Coded aperture snapshot spectral imager (CASSI) has been a popular spectral imaging architecture for its ability of capturing hyperspectral temporal resolution. However, such snapshot imaging system entails a large sacrifice in the spatial resolution of the data cube, since only a small amount of light gets into the imager during one snapshot. Also, the spatial resolution of the CASSI system is limited by the pixel size (and amount) of the detector, while it is difficult to fabricate a dense detector with small pixel size, especially for infrared spectral bands. Super-resolution is an advanced post-processing technique to alleviate such problem by exploiting the prior information of the image. In this letter, we try to realize image super-resolution from the perspective of developing new form of measurements by taking advantage of a modified CASSI system equipped with a coded aperture with higher spatial resolution than the detector, merging the SR model into the hardware configuration. Then the original data cube can be reconstructed from lower resolution measurements, thus the super-resolution is realized during the compressive sensing reconstruction process. The new system can be achieved based on the classical CASSI architecture in two dual ways, one by replacing the coded aperture with a higher resolution one and the other by substituting the focal plane array (FPA) detector with a lower resolution one. The experiments show that, we can recover images of higher quality with the first modification of CASSI system above, simply using a higher resolution coded aperture.
作者: Han Yuan,Fengxia Yan,Xinmeng Chen,Jubo Zhu
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To realize hyperspectral image super-resolution on the hardware level by modifying the CASSI system to enhance spatial resolution through compressive sensing techniques.

The modified SR-CASSI systems, particularly MSR-CASSI with a higher resolution coded aperture, significantly improve spatial resolution in hyperspectral imaging with reduced measurements. Future work could involve advanced algorithms like deep learning and optimized hardware configurations.

The study relies on simulation and limited real data; it uses a simple reconstruction algorithm (TwIST) which may not be optimal, and the coded aperture is randomly generated without optimization. Physical implementation and generalization to other systems are not fully explored.

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