2,960 research outputs found
Image Restoration using Total Variation Regularized Deep Image Prior
In the past decade, sparsity-driven regularization has led to significant
improvements in image reconstruction. Traditional regularizers, such as total
variation (TV), rely on analytical models of sparsity. However, increasingly
the field is moving towards trainable models, inspired from deep learning. Deep
image prior (DIP) is a recent regularization framework that uses a
convolutional neural network (CNN) architecture without data-driven training.
This paper extends the DIP framework by combining it with the traditional TV
regularization. We show that the inclusion of TV leads to considerable
performance gains when tested on several traditional restoration tasks such as
image denoising and deblurring
Deep learning in computational microscopy
We propose to use deep convolutional neural networks (DCNNs) to perform 2D and 3D computational imaging. Specifically, we investigate three different applications. We first try to solve the 3D inverse scattering problem based on learning a huge number of training target and speckle pairs. We also demonstrate a new DCNN architecture to perform Fourier ptychographic Microscopy (FPM) reconstruction, which achieves high-resolution phase recovery with considerably less data than standard FPM. Finally, we employ DCNN models that can predict focused 2D fluorescent microscopic images from blurred images captured at overfocused or underfocused planes.Published versio
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