@misc{oai:repo.qst.go.jp:00079937,
 author = {Sakaki, Hironao and Shiokawa, Keiichiro and Miyatake, Tatsuhiko and Nishiuchi, Mamiko and Kondo, Kotaro and Dover, NicholasPeter and Lowe, HazelFrances and Kon, Akira and Kando, Masaki and Watanabe, Yukinobu and Sakaki, Hironao and Shiokawa, Keiichiro and Miyatake, Tatsuhiko and Nishiuchi, Mamiko and Kondo, Kotaro and Dover, NicholasPeter and Lowe, HazelFrances and Kon, Akira and Kando, Masaki and Watanabe, Yukinobu},
 month = {Apr},
 note = {On the experiment for laser-driven ion acceleration at J-KAREN-P[1] with pulse repetition rates of 0.1 Hz, the angular distribution of electron spectrum is diagnosed by multiple electron spectrometers. The electron spectrometer with comprising a bending magnet, a CCD camera and a scintillator is placed in the main vacuum chamber. When the laser focus on the target, many radiations generated in the main vacuum chamber, therefore, CCD camera is exposed to the radiation and distorted the measured images. We need to develop a new technique for removing noise (denoising) from a noisy image and recovering a true measurement image. 
In recent years, with the development of machine learning methods such as Deep-Learning, "Deep-Learning based Feature filtering" that uses a feature value of an image obtained from machine learning as a base and reproduces a true image from a noisy image is developed[2]. This filtering technique is a new technique of reconstructing a denoising image by separating noise and true data with the feature value of the true-image data. This technique expectes to be effective for the denoising of radiation. In order to demonstrate the feature filtering method, we make a pseudo-measured image generated from an ideal simulation of electron spectroscopy (using as the true-image data), and the noise-image data which make from the measured radiation noise convoluted with the pseudo-measured image  (using as the noise-image data). Then, the ideal feature base is obtained by machine learning from the true-image data and the noise-image data, the feature filtering of the actually measured data is verified by using these the ideal feature base. In this report, we show the denoising performance of the feature filtering compared with "median filter" which is generally used for filtering of radiation noise., HEDS2020},
 title = {Denoising technique of an in-line electron energy spectrometer based on the feature filtering},
 year = {2020}
}