Untrained Low-Rank Neural Network Prior for Multi-Dimensional Image Recovery

Recently, untrained neural network priors (UNNPs) have received increasing attention for multi-dimensional image recovery. However, previous studies are based on over-parameterized untrained neural networks, which results in unstable behavior. In this letter, we propose an untrained low-rank neural network prior (ULRNNP) for multi-dimensional image recovery, which enjoys the powerful representation ability and stable behavior. More specifically, the elaborately designed nonlinear Tucker decomposition module implicitly imposes low-rank constraints on the feature tensor and can more compactly represent the feature tensor. Attributed to the suggested nonlinear Tucker decomposition module, ULRNNP can simultaneously enjoy strong representation ability and stable behavior. The friendly stable behavior allows us to design a friendly stopping criteria without the reference ground truth image as compared with classic UNNP-based methods. Extensive experiments on different multi-dimensional image datasets validate the superior performance of the proposed ULRNNP over state-of-the-art methods.