An inpainting and super resolution method for image mapping spectrometer

Haotian Shao; Lijuan Su; Aniqi Liu; Yan Yuan; Yi Jiang

doi:10.1117/12.2666359

22 May 2023 An inpainting and super resolution method for image mapping spectrometer

Haotian Shao, Lijuan Su, Aniqi Liu, Yan Yuan, Yi Jiang

Proceedings Volume 12327, SPIE Future Sensing Technologies 2023; 123271P (2023) https://doi.org/10.1117/12.2666359
Event: SPIE Future Sensing Technologies, 2023, Yokohama, Japan

Conference Poster

Abstract

Image Mapping Spectrometry (IMS) is a compact snapshot hyperspectral imaging technology. However, the image mapper used in the IMS causes degradation of the reconstructed spectral datacube, such as, low spatial resolution, missing areas and stripe artifacts. In this paper, we propose an end-to-end deep learning method to jointly inpainting and super resolution the restored spectral images of the IMS. The method includes an image inpainting network, which is designed to correct the nonuniform intensity and missing data, and an image super resolution network, which aims to enhance the spatial resolution of images. In addition, a local nonuniformity correction method is proposed to preprocess the IMS images. Simulation and experimental results demonstrate the effectiveness of the proposed method.

Citation Download Citation

Haotian Shao, Lijuan Su, Aniqi Liu, Yan Yuan, and Yi Jiang "An inpainting and super resolution method for image mapping spectrometer", Proc. SPIE 12327, SPIE Future Sensing Technologies 2023, 123271P (22 May 2023); https://doi.org/10.1117/12.2666359

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

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.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available