Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Restoration Method of Hadamard Coding Spectral Imager

Not Accessible

Your library or personal account may give you access

Abstract

Hadamard coding spectral imaging technology is a computational spectral imaging technology, which modulates the target’s spectral information and recovers the original spectrum by inverse transformation. Because it has the advantage of multichannel detection, it is being studied by more researchers. For the engineering realization of push-broom coding spectral imaging instrument, it will inevitably be subjected to push-broom error, template error and detection noise, the redundant sampling problem caused by detector. Therefore, three restoration methods are presented in this paper: firstly, the one is the least squares solution, the two is the zero-filling inverse solution by extending the coding matrix in the redundant coding state to a complete higher order Hadamard matrix, the three is sparse method. Secondly, the numerical and principle analysis shows that the inverse solution of zero-compensation has better robustness and is more suitable for engineering application; its conditional number, error expectation and covariance are better and more stable because it directly uses Hadamard matrix, which has good generalized orthogonality. Then, a real-time spectral reconstruction method is presented, which is based on inverse solution of zero-compensation. Finally, simulation analysis shows that spectral data could be destructed relative accuracy in the error condition; however, the effect of template noise and push error on reconstruction is much greater than that of detection error. Therefore, in addition to reducing the detection noise as much as possible, lower template noise and more accurate push controlling should be guaranteed specifically in engineering realization.

© 2020 The Author(s)

PDF Article
More Like This
Frequency upconversion imaging based on Hadamard coding

YuQi Jiang, WeiJi He, TianYi Mao, GuoHua Gu, and Qian Chen
Opt. Express 29(18) 28741-28750 (2021)

Image Restoration by the Method of Least Squares

Carl W. Helstrom
J. Opt. Soc. Am. 57(3) 297-303 (1967)

Comparison of image restoration methods

T. M. Cannon, H. J. Trussell, and B. R. Hunt
Appl. Opt. 17(21) 3384-3390 (1978)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.