Convolutional-neural-network-based denoising with estimated noise-based normalization to effectively reduce noise for various noise levels
Atsuro Suzuki, Tomoki Amemiya, Yukio Kaneko, Suguru Yokosawa, Toru Shirai · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 2024
To develop a convolutional neural network (CNN)-based denoiser for various noise levels, we propose the use of estimated noise-based normalization in denoising. When the CNN-based denoiser with estimated noise-based normalization was applied to brain FLAIR images with various noise levels, it resulted in values closer to the normalized root mean square error (NRMSE) between the denoised and the target images compared with a conventional CNN-based denoiser trained with the same noise level as that in the input image. In conclusion, our method effectively reduced the noise in an image with various noise levels in terms of minimization of the NRMSE.