Abstract
In this paper, we propose a novel approach to RGB image inpainting, which recovers missing entries of image by using low rank tensor completion. The approach is based on recently proposed tensor train (TT) decomposition, which is used to effectively enforce the low rankness of the image. In addition, our approach exploits the local smooth priors of visual data by incorporating the 2D total variation. Ket augmentation (KA) scheme is used to permute the image to a high order tensor, and then low rankness of balanced KA-TT matrices and total variation (TV) norm constraints are applied to recover the missing entries of the image. In order to reduce the computational complexity, in the proposed approach, nuclear norm is replaced by minimum Frobenius norm of two factorization matrices, which reduces the time for singular value decomposition (SVD). Lastly, in order to solve the proposed model, the efficient alternating direction method of multipliers (ADMM) is developed. The results of image inpainting experiments demonstrate the significantly superior performance of our approach.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.