Affiliation:
1. Beijing Institute of Technology
2. University of Delaware
Abstract
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.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Shanghai Municipal Science and Technology Major Project
Shanghai Science and Technology Innovation Action Plan Project
Fundamental Research Funds for the Central Universities
Subject
Atomic and Molecular Physics, and Optics
Cited by
2 articles.
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