Application of Low-Rank Tensor Completion Combined with Prior Knowledge in Visual Data
Journal Title: Acadlore Transactions on AI and Machine Learning - Year 2024, Vol 3, Issue 4
Abstract
In recent years, representing computer vision data in tensor form has become an important method of data representation. However, due to the limitations of signal acquisition devices, the actual data obtained may be damaged, such as image loss, noise interference, or a combination of both. Using Low-Rank Tensor Completion (LRTC) techniques to recover missing or corrupted tensor data has become a hot research topic. In this paper, we adopt a tensor coupled total variation (t-CTV) norm based on t-SVD as the minimization criterion to capture the combined effects of low-rank and local piecewise smooth priors, thus eliminating the need for balance parameters in the process. At the same time, we utilize the Non-Local Means (NLM) denoiser to smooth the image and reduce noise by leveraging the nonlocal self-similarity of the image. Furthermore, an Alternating Direction Method of Multipliers (ADMM) algorithm is designed for the proposed optimization model, NLM-TCTV. Extensive numerical experiments on real tensor data (including color, medical, and satellite remote sensing images) show that the proposed method has good robustness, performs well in noisy images, and surpasses many existing methods in both quality and visual effects.
Authors and Affiliations
Junzhe Zhao, Huimin Wang
Keywords
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- EP ID EP755408
- DOI https://doi.org/10.56578/ataiml030405
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