Robust and adaptive subspace learning for fast hyperspectral image denoising
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10489-024-05762-x.pdf
Reference46 articles.
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3. He W, Yao Q, Li C, Yokoya N, Zhao Q, Zhang H, Zhang L (2022) Non-local meets global: an iterative paradigm for hyperspectral image restoration. IEEE Trans Pattern Anal Mach Intell 44(4):2089–2107. https://doi.org/10.1109/TPAMI.2020.3027563
4. Dabov K, Foi A, Katkovnik V, Egiazarian K (2007) Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans Image Process 16(8):2080–2095. https://doi.org/10.1109/TIP.2007.901238
5. Fan H, Li C, Guo Y, Kuang G, Ma J (2018) Spatial-spectral total variation regularized low-rank tensor decomposition for hyperspectral image denoising. IEEE Trans Geosci Remote Sens 56(10):6196–6213. https://doi.org/10.1109/TGRS.2018.2833473
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