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Conference Papers Year : 2021

A residual U-Net network with image prior for 3D image denoising


Denoising algorithms via sparse representation are among the state-of-the art for image restoration. On previous work, we proposed SPADE-a sparse-and prior-based method for 3D-image denoising. In this work, we extend this idea to learning approaches and propose a novel residual-U-Net prior-based (ResPrU-Net) method that exploits a prior image. The proposed ResPrU-Net architecture has two inputs, the noisy image and the prior image, and a residual connection that connects the prior image to the output of the network. We compare ResPrU-Net to U-Net and SPADE on human knee data acquired on a spectral computerized tomography scanner. The prior image is built from the noisy image by combining information from neighbor slices and it is the same for both SPADE and ResPrU-Net. For deep learning approaches, we use four knee samples and data augmentation for training, one knee for validation and two for test. Results show that for high noise, U-Net leads to worst results, with images that are excessively blurred. Prior-based methods, SPADE and ResPrU-Net, outperformed U-Net, leading to restored images that present similar image quality than the target. ResPrU-Net provides slightly better results than SPADE. For low noise, methods present similar results.
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hal-02500664 , version 1 (06-03-2020)



Juan F P J Abascal, S Bussod, N. Ducros, S. Si-Mohamed, P. Douek, et al.. A residual U-Net network with image prior for 3D image denoising. 28th European Signal Processing Conference (EUSIPCO), Jan 2021, Amsterdam, Netherlands. ⟨10.23919/Eusipco47968.2020.9287607⟩. ⟨hal-02500664⟩
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