Deep Neural Networks for Low-resolution Photon-limited Imaging

In this paper, we implement deep learning methods to recover downsampled noisy signals often present in compressed sensing applications. As an alternative to relying on previously established optimization based algorithms, we implement stacked denoising autoencoders and convolutional neural networks to perform signal reconstructions. Moreover, we propose a Poisson autoencoder inverting network (PAIN) architecture to reconstruct compressed signals imposed with Poisson noise. We observe less computational costs associated with this method while improving on reconstructions from a traditional stacked denoising autoencoder and remaining competitive with a more complex architecture in terms of Mean Squared Error (MSE). We train all proposed architectures on the MNIST dataset and establish deep neural networks as a reconstruction method.