Abstract
The research on hyperspectral images (HSIs) has attracted the attention of many scholars in recent years due to its wide applications, e.g., soil and mineral properties analysis. A main challenge in hyperspectral imaging is the various noise disturbances encountered during the image analysis process. Therefore, denoising of HSIs is an initial but essential step to facilitate further analytics steps. In the literature, many denoising methods based on low-rank representations have been applied in the study of HSIs. This paper proposes a structure-preserving method based on dual low-rank, and we demonstrate that our proposed method achieves better denoising results (qualitatively and quantitatively) on both simulated and real datasets, compared to related work.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China under Grant No. 61702251, the Natural Science Basic Research Plan in Shaanxi Province of China under Program No. 2018JM6030, in part by the Natural Sciences and Engineering Research Council of Canada, and Youth Academic Talent Support Program of Northwest University under Grant No. 360051900151.
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Tang, M., Leng, C., Cheng, I. (2022). Hyperspectral Image Denoising Based on Dual Low-Rank Structure Preservation. In: Berretti, S., Su, GM. (eds) Smart Multimedia. ICSM 2022. Lecture Notes in Computer Science, vol 13497. Springer, Cham. https://doi.org/10.1007/978-3-031-22061-6_32
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DOI: https://doi.org/10.1007/978-3-031-22061-6_32
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