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Hyperspectral image reconstruction via patch attention driven network
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AI
DOI：10.1364/OE.479549.png)
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Abstract
En 中文
Coded aperture snapshot spectral imaging (CASSI) captures 3D hyperspectral images (HSIs) with 2D compressive measurements. The recovery of HSIs from these measurements is an ill-posed problem. This paper proposes a novel, to our knowledge, network architecture for this inverse problem, which consists of a multilevel residual network driven by patch-wise attention and a data pre-processing method. Specifically, we propose the patch attention module to adaptively generate heuristic clues by capturing uneven feature distribution and global correlations of different regions. By revisiting the data pre-processing stage, we present a complementary input method that effectively integrates the measurements and coded aperture. Extensive simulation experiments illustrate that the proposed network architecture outperforms state-of-the-art methods.
Keywords:
CODED-APERTURE DESIGN
RESOLUTION
Journal
IF:
3.3
Papers:
6.1W
Citations:
14.3W