Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction. (arXiv:2205.07471v2 [eess.IV] UPDATED)

Web: http://arxiv.org/abs/2205.07471

Inspired by the great success of deep neural networks, learning-based methods
have gained promising performances for metal artifact reduction (MAR) in
computed tomography (CT) images. However, most of the existing approaches put
less emphasis on modelling and embedding the intrinsic prior knowledge
underlying this specific MAR task into their network designs. Against this
issue, we propose an adaptive convolutional dictionary network (ACDNet), which
leverages both model-based and learning-based methods. Specifically, we explore
the prior structures of metal artifacts, e.g., non-local …

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