Online deconvolution for pushbroom hyperspectral imaging systems
Overview & Synopsis
This paper introduces a framework based on the LMS algorithm for sequential deconvolution of hyperspectral images acquired by industrial pushbroom imaging systems. Considering a sequential model of image blurring phenomenon, we derive a sliding-block zero-attracting LMS algorithm with spectral regularization. The role of each hyper-parameter is discussed. The performance of the algorithm is evaluated using real hyperspectral data.
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Document Information
| Document Title | Online deconvolution for pushbroom hyperspectral imaging systems |
|---|---|
| Author / Organization | Yingying Song, El-Hadi Djermoune, Jie Chen, Cédric Richard, David Brie |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2017 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-72802 |
| Original Source Link | Visit Source Page on hal.science » |