Deep plug-and-play for tensor robust principal component analysis

Tensor Robust Principal Component Analysis (TRPCA) aims at recovering the low-rank and sparse components from target tensor, which has extensive applications in multi-dimensional data recovery. However, most of the existing methods only exploit the global low-rank of image data, which result in missing local details in the recovered data. To restore the data more accurately, we propose a new TRPCA method which simultaneously combines the model-based method and data-driven approaches to preserve the global structure and fine local information. Specially, we pick the tensor nuclear norm to characterize the global correlation and a convolutional neural network(CNN) denoiser which reserves the local detail. Then, a flexible alternating direction method of multipliers (ADMM) is designed to deal with the proposed optimization model. Extensive experiments on various types of tensor data illustrate that our model enhances performance compared to state-of-the-art methods.