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De-Noising of Photoacoustic Microscopy Images by Deep Learning
In this work, we propose a deep learning-based method to remove complex noise from PAM images without mathematical priors and manual selection of settings for different input images.
Photoacoustic Microscopy with Sparse Data Enabled by Convolutional Neural Networks for Fast Imaging
The results show that the model can enhance the image quality of the sparse PAM image of blood vessels from several aspects, which may help fast PAM and facilitate its clinical applications.
Adaptive Weighting Depth-variant Deconvolution of Fluorescence Microscopy Images with Convolutional Neural Network
The adaptive weighting of the patch-wise deconvolved image can eliminate patch boundary artifacts and improve deconvolved image quality.
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