Coarse to Fine Two-Stage Approach to Robust Tensor Completion of Visual Data.

Area of Science:

• Computer Vision
• Data Science
• Machine Learning

Background:

• Tensor completion estimates missing data but struggles with outliers.
• Existing robust methods assume sparse corruption, which is often unrealistic.
• Visual data frequently exhibits substantial outlier contamination.

Purpose of the Study:

• To develop a robust tensor completion method for visual data with extensive outlier corruption.
• To address the limitations of existing methods that assume sparse corruption.
• To improve the accuracy and reliability of tensor completion in real-world scenarios.

Main Methods:

• A two-stage, coarse-to-fine framework for robust tensor completion.
• Utilizing a global coarse completion to guide local patch refinement.
• An M-estimator-based robust tensor ring recovery for outlier identification and mitigation.

Main Results:

• The proposed method demonstrates superior performance in tensor completion with gross corruption.
• Effective identification and alleviation of a large number of outliers.
• Outperforms state-of-the-art robust tensor completion algorithms.

Conclusions:

• The novel two-stage approach effectively handles extensive outlier corruption in tensor completion.
• The M-estimator-based method provides adaptive outlier mitigation for improved data recovery.
• This work advances robust tensor completion for challenging visual data applications.