Search Results for author:
Found 7 papers, 4 papers with code
Robust single-shot 3D fluorescence imaging in scattering media with a simulator-trained neural network
Broadly, we believe that our simulator-based deep learning approach can be applied to a wide range of imaging through scattering techniques where experimental paired training data is lacking.
Block Modulating Video Compression: An Ultra Low Complexity Image Compression Encoder for Resource Limited Platforms
We consider the image and video compression on resource limited platforms.
Reliable deep-learning-based phase imaging with uncertainty quantification
We believe our uncertainty learning framework is widely applicable to many DL-based biomedical imaging techniques for assessing the reliability of DL predictions.
Deep speckle correlation: a deep learning approach towards scalable imaging through scattering media
Importantly, instead of characterizing a single input-output relation of a fixed medium, we train our CNN to learn statistical information contained in several scattering media of the same class.
Deep learning approach to Fourier ptychographic microscopy
Specifically, we show that it is possible to reconstruct high-SBP dynamic cell videos by a CNN trained only on the first FPM dataset captured at the beginning of a time-series experiment.
Catadioptric HyperSpectral Light Field Imaging
In this paper, we present a single camera hyperspectral light field imaging solution that we call Snapshot Plenoptic Imager (SPI).
