Name
Abstract | ||
|---|---|---|
This paper proposes a hyperspectral image deconvolution algorithm for the online restoration of hyperspectral images as provided by wiskbroom and pushbroom scanning systems. We introduce a least-mean-squares (LMS)-based framework accounting for the convolution kernel noncausality and including nonquadratic (zero attracting and piecewise constant) regularization terms. This results in the so-called sliding block regularized LMS (SBR-LMS), which maintains a linear complexity compatible with real-time processing in industrial applications. A model for the algorithm mean and mean-squares transient behavior is derived and the stability condition is studied. Experiments are conducted to assess the role of each hyper-parameter. A key feature of the proposed SBR-LMS is that it outperforms standard approaches in low SNR scenarios such as ultra-fast scanning. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1137/18M1177640 | SIAM JOURNAL ON IMAGING SCIENCES |
Keywords | Field | DocType |
hyperspectral image,online deconvolution,LMS,ZA-LMS | Mathematical optimization,Deconvolution,Algorithm,Hyperspectral imaging,Digital imaging,Pixel,Image restoration,Kernel (image processing),Image resolution,Data cube,Mathematics | Journal |
Volume | Issue | ISSN |
12 | 1 | 1936-4954 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yingying Song | 1 | 11 | 1.92 |
| El-Hadi Djermoune | 2 | 35 | 9.87 |
| Jie Chen | 3 | 34 | 11.39 |
| Cédric Richard | 4 | 940 | 71.61 |
| David Brie | 5 | 130 | 24.28 |