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Abstract #2797

Noise Level Adaptive Deep Convolutional Neural Network for Image Denoising

Kenzo Isogawa1, Takashi Ida1, Taichiro Shiodera1, Tomoyuki Takeguchi1, Yuichi Yamashita2, and Hiroshi Takai3

1Corporate research and development center, Toshiba corporation, Kawasaki, Japan, 2MRI system division, Toshiba Medical Systems Corporation, Otawara, Japan, 3MRI Systems Development Department, Toshiba Medical Systems Corporation, Otawara, Japan

For integrated diagnosis, MRI provides various types of images related to different acquisition parameters. The change of the acquisition parameters affects noise levels of the provided image in meaningful ways. To adapt the change of the noise level, it is desirable for denoising methods to be adaptive to the noise level, but deep neural network methods are not adaptive, despite their high performance. We propose a deep convolutional neural network (CNN) adjustable to noise levels. The activation functions of the CNN use soft shrinkage whose threshold is proportional to noise level of the input image.

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