A Comparative Study of Video Inpainting Techniques

Video Inpainting is an interesting and active subject in image and video processing. The objective of video inpainting techniques is the reconstruction of the missing holes after object removal in an unnoticeable form. Current video inpainting techniques are in general computationally complex due to the extensive search to find the most similar patch to fill in the missing frames. Moreover, unsatisfactory results appear when the missing hole is large. In this paper, we study various video inpainting techniques with respect to the quality of results and processing time. Furthermore, we conduct a comparative study between four techniques on the same video scenes and hardware. Strengths and drawbacks of each technique are discussed based on the open challenges.

[1]  Mohsen Soryani,et al.  Video object inpainting: a scale-robust method , 2012 .

[2]  Jenq-Neng Hwang,et al.  Exemplar-Based Video Inpainting Without Ghost Shadow Artifacts by Maintaining Temporal Continuity , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[3]  Guillermo Sapiro,et al.  Navier-stokes, fluid dynamics, and image and video inpainting , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[4]  Margarita N. Favorskaya,et al.  Intelligent Texture Reconstruction of Missing Data in Video Sequences Using Neural Networks , 2012, KES.

[5]  I. Faye,et al.  Static object removal from video scene using local similarity , 2013, 2013 IEEE 9th International Colloquium on Signal Processing and its Applications.

[6]  Guillermo Sapiro,et al.  Video inpainting of occluding and occluded objects , 2005, IEEE International Conference on Image Processing 2005.

[7]  P. J. Narayanan,et al.  Video Completion for Indoor Scenes , 2006, ICVGIP.

[8]  Patrick Pérez,et al.  Region filling and object removal by exemplar-based image inpainting , 2004, IEEE Transactions on Image Processing.

[9]  Eli Shechtman,et al.  Space-time video completion , 2004, CVPR 2004.

[10]  Timothy K. Shih,et al.  Video Inpainting on Digitized Vintage Films via Maintaining Spatiotemporal Continuity , 2011, IEEE Transactions on Multimedia.

[11]  Mohan S. Kankanhalli,et al.  Automatic video logo detection and removal , 2005, Multimedia Systems.

[12]  Guillermo Sapiro,et al.  Image inpainting , 2000, SIGGRAPH.

[13]  Tai-Pang Wu,et al.  Video repairing under variable illumination using cyclic motions , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Nizar Bouguila,et al.  Video Completion Using Bandlet Transform , 2012, IEEE Transactions on Multimedia.

[15]  Tai-Pang Wu,et al.  Video repairing: inference of foreground and background under severe occlusion , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[16]  Changsheng Xu,et al.  Automatic TV Logo Detection, Tracking and Removal in Broadcast Video , 2007, MMM.

[17]  Anil Kokaram,et al.  Stereo video inpainting , 2011, Electronic Imaging.

[18]  Eli Shechtman,et al.  Space-Time Completion of Video , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Guillermo Sapiro,et al.  Video Inpainting Under Constrained Camera Motion , 2007, IEEE Transactions on Image Processing.

[20]  Mubarak Shah,et al.  Motion Layer Based Object Removal in Videos , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[21]  Dayang Rohaya,et al.  Fast and Efficient Video Completion Using Object Prior Position , 2013, IVIC.

[22]  Jian Zhao,et al.  Efficient Object-Based Video Inpainting , 2006, 2006 International Conference on Image Processing.

[23]  Nizar Bouguila,et al.  Automatic Inpainting Scheme for Video Text Detection and Removal , 2013, IEEE Transactions on Image Processing.

[24]  B. Vidhya,et al.  Novel video inpainting using patch sparsity , 2011, 2011 International Conference on Recent Trends in Information Technology (ICRTIT).

[25]  Dayang Rohaya,et al.  Fast and efficient multichannel image completion using local similarity , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[26]  Chi-Keung Tang,et al.  Image repairing: robust image synthesis by adaptive ND tensor voting , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..