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Abstract #2628

Parallelized Blind MR Image Denoising using Deep Convolutional Neural Network

satoshi ITO1, taro SUGAI1, kohei TAKANO1, and shohei OUCHI1
1Utsunomiya University, Utsunomiya, Japan

To improve the denoising performance of a convolutional neural network (CNN), a parallelized blind image denoising (ParBID) was proposed and demonstrated. ParBID procedure is similar to SENSE technique, 1) linear combination of adjacent 2D sliced noisy images, 2) blind noise level CNN denoising, and 3) separation of linearly combined and denoised images by solving linear equation. Experimental studies showed that the PSNR and the SSIM were improved for all noise levels, from 2.5% to 7.5%. ParBID showed that the greatest PSNR improvements were obtained when three slice images were used for linear image combination.

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