2019-10-15-hara19a.md

title	crossref	abstract	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
SPoD-Net: Fast Recovery of Microscopic Images Using Learned ISTA	acml19	Recovering high quality images from microscopic observations is an essential technology in biological imaging. Existing recovery methods require solving an optimization problem by using iterative algorithms, which are computationally expensive and time consuming. The focus of this study is to accelerate the image recovery by using deep neural networks (DNNs). In our approach, we first train a certain type of DNN by using some observations from microscopes, so that it can well approximate the image recovery process. The recovery of a new observation is then computed thorough a single forward propagation in the trained DNN. In this study, we specifically focus on observations obtained by SPoD (Super-resolution by Polarization Demodulation), a recently developed microscopic technique, and accelerate the image recovery for SPoD by using DNNs. To this end, we propose \emph{SPoD-Net}, a specifically tailored DNN for fast recovery of SPoD images. Unlike general DNNs, SPoD-Net can be parameterized using a small number of parameters, which is helpful in two ways: (i) it can be stored in a small memory, and (ii) it can be trained efficiently. We also propose a method to stabilize the training of SPoD-Net. In the experiments with the real SPoD observations, we confirmed the effectiveness of SPoD-Net over existing recovery methods. Specifically, we observed that SPoD-Net could recover images with more than a hundred times faster than the existing method.	inproceedings	Proceedings of Machine Learning Research	hara19a	0	SPoD-Net: Fast Recovery of Microscopic Images Using Learned ISTA	694	709	694-709	694	false	Hara, Satoshi and Chen, Weichih and Washio, Takashi and Wazawa, Tetsuichi and Nagai, Takeharu	given family Satoshi Hara given family Weichih Chen given family Takashi Washio given family Tetsuichi Wazawa given family Takeharu Nagai	2019-10-15		PMLR	Proceedings of The Eleventh Asian Conference on Machine Learning	101	inproceedings	date-parts 2019 10 15	http://proceedings.mlr.press/v101/hara19a/hara19a.pdf