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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.
Adherent Mist and Raindrop Removal from a Single Image Using Attentive Convolutional Network
In this work, we newly present a problem of image degradation caused by adherent mist and raindrops.
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.
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