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Express"],"published-print":{"date-parts":[[2026,3,23]]},"abstract":"<jats:p>Holographic tomography based on multi-angle reconstruction of complex amplitude field distributions can retrieve the internal refractive-index distribution of the object under investigation. However, its spatial resolution and imaging quality are often limited by optical aberrations. Traditional aberration correction methods face limitations such as high cost and vulnerability to abnormal conditions. To address these challenges, we propose a dual-output-branch three-dimensional convolutional neural network that optimizes the input complex-amplitude distributions acquired under multi-angle illumination via end-to-end learning, thereby enabling accurate aberration correction for tomographic imaging. Taking multi-angle phase maps and masks as inputs, the network performs full-angle aberration correction via the complementary outputs of Zernike coefficients and aberration phase maps. 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