Abstract
Fluorescence microscopy methods are an important imaging technique in cell biology. Due to their depth sensitivity they allow a direct 3-D imaging. However, the resulting volume data sets are undersampled in depth, and the 2-D slices are blurred and noisy. Reconstructing the full 3-D information from these data is therefore a challenging task, and of high relevance for biological applications. We address this problem by combining deconvolution of the 3-D data set with interpolation of additional slices in an integrated variational approach. Our novel 3-D reconstruction model, Interpolating Robust and Regularised Richardson-Lucy reconstruction (IRRRL), merges the Robust and Regularised Richardson-Lucy deconvolution (RRRL) from [16] with variational interpolation. In this paper we develop the theoretical approach and its efficient numerical implementation using Fast Fourier Transform and a coarse-to-fine multiscale strategy. Experiments on confocal fluorescence microscopy data demonstrate the high restoration quality and computational efficiency of our approach.
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Elhayek, A., Welk, M., Weickert, J. (2011). Simultaneous Interpolation and Deconvolution Model for the 3-D Reconstruction of Cell Images. In: Mester, R., Felsberg, M. (eds) Pattern Recognition. DAGM 2011. Lecture Notes in Computer Science, vol 6835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23123-0_32
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DOI: https://doi.org/10.1007/978-3-642-23123-0_32
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