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A flat-field correction method is proposed for multiple measured hyperspectral microscopy imaging in this paper. As the most crucial preprocessing process in quantitative microscopic analysis, flat-field correction solves the uneven illumination caused by vignetting in microscopic images, and guarantees the precision of spatial and spectral information in hyperspectral microscopic imaging. In order to carry out flat-field correction and extract uneven illumination among groups of hyperspectral microscopic data containing hundreds of bands simultaneously, two properties of vignetting have been exploited: i) low-rank property is reflected by little information contained in vignetting; ii) local smoothness can be observed as a gradual change in brightness of vignetting, which is typically equivalent to the sparseness in spatial frequency domain. Combining the two properties above, a novel Low Rank Smooth Flat-field Correction (LRSFC) model modified from common orthogonal basis extraction is proposed, while an optimization is solved based on alternating direction multiplier method (ADMM), obtaining a unique flat-field term with low-rank and smooth properties. Qualitative and quantitative experimental assessments indicate that LRSFC does not add extra cell texture to the extracted flat-field term, whose performance appears prior to other state-of-the-art flat-field correction methods. [ABSTRACT FROM AUTHOR]

Hyperspectral (HS) satellite data have been widely applied in many fields due to its numerous bands. Along with the advantages of high spectral resolution, HS satellite data are still limited by some disadvantages of high acquisition cost, low revisiting capability, and low spatial resolution. Compared with HS satellites, multispectral (MS) satellites have a large number, large width, strong coverage, and high spatial resolution. Therefore, MS data can be used as the input to the spectral reconstruction (SR) to obtain HS data with high temporal resolution. Better HS data can be obtained by spectral reconstructing with these continuous multitemporal data than with single-temporal data. A multitemporal spectral reconstruction network (MTSRN) is proposed in this article, which is used to reconstruct HS images from multitemporal MS images. The proposed MTSRN comprises multiple single-temporal spectral reconstruction networks (STSRN) for extracting temporal features and a multitemporal fusion network (MTFN). The parallel component alternative (PA) post-processing method enhances the physical plausibility of reconstructed HS data. To demonstrate performance of the proposed method in aspects of multitemporal reconstruction, experiments are conducted on four multitemporal HS and MS satellite datasets. The experimental results prove that the proposed MTSRN obtains better SR results compared with the SR method based on single-temporal information. [ABSTRACT FROM AUTHOR]