Feasibility study on noise reduction from images using deep learning to improve spatial awareness in remote operation

Tanifuji, Yuta ; Hanari, Toshihide ; Kawabata, Kuniaki   

In this paper, we describe the results of a feasibility study of a noise reduction method from images using deep learning technology for decommissioning work. Currently, remotely operated robots have been used for the decommissioning work at the Fukushima Daiichi Nuclear Power Station (FDNPS) due to the high radiation environment. We have been conducting research and development for providing clear images during operations by removing only noise from images containing noise to contribute to safe and secure decommissioning work. Since we do a feasibility study of the noise reduction method using deep learning, the main target is not the video, but rather images, which are components of the video. We adopted the approach of building a learning model that can cope with various types of noise by training many noisy images in the deep learning process.