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Denoiser Algorithms with DNNs

Denoiser Algorithms with DNNs. Alessandro Sbandi. Deep Learning Methods. a) Original image b) Image+ GaussianNoise ( σ =50) c) Denoised image with new network PSNR=24.74 dB d) Denoised image with conventional denoiser PSNR=24.39 dB.

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Denoiser Algorithms with DNNs

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  1. Denoiser Algorithms with DNNs Alessandro Sbandi

  2. Deep Learning Methods a) Original image b) Image+ GaussianNoise (σ=50) c) Denoised image with new network PSNR=24.74 dB d) Denoised image with conventional denoiser PSNR=24.39 dB • Successfully used for image classification, object detection, image restoration, super-resolution, etc. • Deep structures combined with large datasets. • Recent DNNs outperform conventional denoiser (state-of-the-art for years).

  3. Deep Neural Networks • Convolutional neural networks (CNNs) learn local patterns in small 2D windows. • Generative Models: -Variational Autoencoder (VAE) -Generative Adversarial Network (GAN)

  4. Possible future neural networks • Auto-Supervised network (without ground-truth) • SAGAN network (GAN with sharpness detection network) • GAN+CNN • VAE autoregressive PSNR=35.82 dB PSNR=28.86 dB

  5. VAE autoregressive vs VAE • Usually latent gaussian variables z, which outputs parameters for our variables. • VAE Gaussian distribution: • Using ‘reparametrization trick’ on a VAE trying to model ‘every’ distribution. • Using invertible transforms, final density of z: • Not anymore gaussian independent variables.

  6. Simulation of MRI for testing denoising algorithms: A plan • Generate MRI via MRiLab software. • Testing denoisers with generated images, then use real ones. F. Liu, J.V. Velikina, W.F. Block, R. Kijowski, A.A. Samsonov. Fast Realistic MRI Simulations Based on Generalized Multi-Pool Exchange Tissue Model. IEEE Transactions on Medical Imaging. 2016. doi: 10.1109/TMI.2016.2620961

  7. Simulation of MRI for testing denoising algorithms: A plan SNR = 1% ~ 10 SNR <<1%

  8. Simulation of MRI for testing denoising algorithms: B plan Noise Distribution in Low SNR MRI • Rician distribution dominates MRI noise for SNR < 2. • Setting / < 2 , calculating cumulative distribution function and inverting it, to obtain and then generate it according to Rice distribution.

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