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Found 5 papers, 1 papers with code
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.
Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions
The experimental results demonstrate the superiority of the proposed method, which not only outperforms all baselines -with the highest F1 scores of 87. 40% on the building change detection dataset and 92. 94% on the change detection dataset -but also obtains the best accuracies on experiments performed with images having a 4x and 8x resolution difference.
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.
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.
