Coded aperture snapshot spectral imaging (CASSI) is a technique that can capture 3D hyperspectral images (HSIs) of scenes in a single shot. However, the quality of the reconstructed HSIs is affected by various optical aberrations and system noise. Existing deep learning methods for HSI reconstruction do not consider these degradation patterns and thus lack generalization ability to real CASSI data. In this paper, we propose a practical method to recover high-quality HSIs from low-quality CASSI data. We use a spectral imaging simulation to generate authentic training data that reflects the optical aberrations of the CASSI system. We then train a generative network on this data to remove blur and chromatic aberrations from the CASSI measurements. Our experiments show that our method can effectively improve the quality of the reconstructed HSIs and can be easily applied to real CASSI systems.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.