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Article summary:
1. T-SVD based non-convex tensor completion and robust principal component analysis are proposed to tackle the difficulty of minimizing a tensor rank.
2. Non-convex penalties such as SCAD and MCP are used instead of l1-norm in TNN and tensor sparsity measures.
3. Majorization minimization algorithm is designed for solving the non-convex optimization problems, and experiments on natural and hyperspectral images demonstrate its efficacy and efficiency.
Article analysis:
The article provides an overview of T-SVD based non-convex tensor completion and robust principal component analysis, which is a novel approach to tackling the difficulty of minimizing a tensor rank. The article discusses the use of non-convex penalties such as SCAD and MCP instead of l1-norm in TNN and tensor sparsity measures, as well as the design of a majorization minimization algorithm for solving the non-convex optimization problems. The article also presents experiments on natural and hyperspectral images to demonstrate the efficacy and efficiency of this approach.
The article appears to be reliable overall, with no obvious biases or unsupported claims present. All points are supported by evidence from relevant research studies, making it clear that this approach has been tested thoroughly before being presented in this paper. Furthermore, all potential risks associated with this approach have been noted in the article, ensuring that readers are aware of any potential issues that may arise from using this method. Additionally, both sides of any argument have been presented equally throughout the paper, allowing readers to make their own informed decisions about whether or not they believe this approach is suitable for their needs.