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• W4296873530 abstract "Super-resolution reconstruction of medical images effectually enhances visual qualities to provide clear visions on anatomical structures. However, the spatial information, which abounds in low-resolution images, has received scant attention in the task of super-resolution, resulting in suppressed reconstruction qualities. This paper brings the spatial information into effective action by virtue of high-dimensional structures of tensor-format data, and presents a method named <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>Tensor Decomposition based medical Image Super-resolution reconstruction</i> (TDIS). In particular, TDIS employs tensors to preserve rich image spatial information and effectually processes the spatial information contained in image tensors by reformulating transposed convolutional layers using tensor decomposition. Moreover, TDIS provides tensor based error terms to capture spatial differences between generated and target images to reduce visual contrasts between them. Experimental results about reconstructing a range of medical images empirically demonstrate the competence of TDIS compared to the state-of-the-art methods." @default.
• W4296873530 created "2022-09-24" @default.
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• W4296873530 date "2022-01-01" @default.
• W4296873530 modified "2023-10-16" @default.
• W4296873530 title "Spatial Information Regularized Tensor Decomposition Framework for Super-Resolution Reconstruction of Medical MRI and Radiographs" @default.
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• W4296873530 doi "https://doi.org/10.1109/tci.2022.3209099" @default.
• W4296873530 hasPublicationYear "2022" @default.
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