Abstract
This paper studies the video inpainting method based on tensor train (TT) decomposition framework. Since TT decomposition works better for the low rank representation on higher-order tensors, we first use a tensor augmentation scheme to generate a higher order tensor from the damaged video. Then, we exploit low TT rankness to recover the damaged video. In addition, according that the time domain smoothing corresponds to the frequency domain sparsity, we combine the sparsity of Fourier domain along the time dimension to construct the inpainting model. Finally, the proposed video inpainting model combining low TT rankness and sparsity in frequency domain is solved by the efficient alternating direction method of multipliers (ADMM) algorithm. The experimental results verify that the proposed method can achieve superior video inpainting results, and can improve the PSNR of video inpainting results better than the methods only enforcing low TT rankness or sparsity.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
