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Volume: 32 | Article ID: art00017
Background subtraction in diffraction X-ray images using deep CNN
  DOI :  10.2352/ISSN.2470-1173.2020.14.COIMG-344  Published OnlineJanuary 2020

Diffraction X-ray images provide molecular level information in tissue under hydrated, physiological conditions at the physiologically relevant millisecond time scale. When processing diffraction x-ray images there is a need to subtract background produced during the capture process prior to making measurements. This is a non-uniform background that is strongest at the diffraction center and decays with increased distance from the center. Existing methods require careful parameter selection or assume a specific background model. In this paper we propose a novel approach for background subtraction in which we learn to subtract background based on labeled examples. The labeled examples are image pairs where in each pair one of the images has diffraction background and the second has the background removed. Using a deep convolutional neural network (CNN) we learn to map an image with background to an image without it. Experimental results demonstrate that the proposed approach is capable of learning background removal with results close to ground truth data (PSNR > 68, SSIM > 0.99) and without having to manually select background parameters.

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Rodrigo Aranguren Carmona, Gady Agam, Thomas Irving, "Background subtraction in diffraction X-ray images using deep CNNin Proc. IS&T Int’l. Symp. on Electronic Imaging: Computational Imaging XVIII,  2020,  pp 344-1 - 344-6,

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