Tensor Error Correction for Corrupted Values in Visual Data

IEEE Computer Society
Publication Type:
Conference Proceeding
2010 IEEE International Conference on Image Processing ICIP 2010 - Proceedings, 2010, pp. 2321 - 2324
Issue Date:
Full metadata record
Files in This Item:
Filename Description SizeFormat
2009007756OK.pdf918.4 kBAdobe PDF
The multi channel image or the video clip has the natural form of tensor. The value of the tensor can be corrupted due to noise in acquisition process. We consider the problem of recovering a tensor L of visual data from its corrupted observations X=L+S, where the corrupted entries S are unknown and unbounded, but are assumed to be sparse. Our work is built on the recent studies about the recovery of corrupted low-rank matrix via trace norm minimization. We extend the matrix case to be tensor case by the definition of tensor trace norm in (6). Furthermore, the problem of tensor is formulated as a convex optimization, which is much harder than its matrix form. Thus, we develop a high quality algorithm to efficiently solve the problem. Our experiments show potential applications of our method and indicate a robust and reliable solution.
Please use this identifier to cite or link to this item: