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Fluorescence microscopy plays an important role in biomedical research. Thedepth-variant point spread function (PSF) of a fluorescence microscope produceslow-quality images especially in the out-of-focus regions of thick specimens.Traditional deconvolution to restore the out-of-focus images is usuallyinsufficient since a depth-invariant PSF is assumed. This article aims athandling fluorescence microscopy images by learning-based depth-variant PSF andreducing artifacts. We propose adaptive weighting depth-variant deconvolution(AWDVD) with defocus level prediction convolutional neural network (DelpNet) torestore the out-of-focus images. Depth-variant PSFs of image patches can beobtained by DelpNet and applied in the afterward deconvolution. AWDVD isadopted for a whole image which is patch-wise deconvolved and appropriatelycropped before deconvolution. DelpNet achieves the accuracy of 98.2%, whichoutperforms the best-ever one using the same microscopy dataset. Image patchesof 11 defocus levels after deconvolution are validated with maximum improvementin the peak signal-to-noise ratio and structural similarity index of 6.6 dB and11%, respectively. The adaptive weighting of the patch-wise deconvolved imagecan eliminate patch boundary artifacts and improve deconvolved image quality.The proposed method can accurately estimate depth-variant PSF and effectivelyrecover out-of-focus microscopy images. To our acknowledge, this is the firststudy of handling out-of-focus microscopy images using learning-baseddepth-variant PSF. Facing one of the most common blurs in fluorescencemicroscopy, the novel method provides a practical technology to improve theimage quality.

Adaptive Weighting Depth-variant Deconvolution of Fluorescence Microscopy Images with Convolutional Neural Network