Open AccessAn Enhanced Video Inpainting Technique with Grey Wolf Optimization for Object Removal Application
Author(s) -
B. Janardhana Rao,
Y. Chakrapani,
Sunil Kumar
Publication year - 2022
Publication title -
journal of mobile multimedia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.229
H-Index - 12
eISSN - 1550-4654
pISSN - 1550-4646
DOI - 10.13052/jmm1550-4646.1835
Subject(s) - inpainting , artificial intelligence , computer vision , computer science , peak signal to noise ratio , video denoising , process (computing) , video tracking , similarity (geometry) , motion blur , computation , object (grammar) , image (mathematics) , algorithm , multiview video coding , operating system
Video inpainting is the most trending research topic from the last decade. Video inpainting is the process of restoring the damaged parts of the vintage video or the filling of the regions by removing the unwanted objects with sophisticated techniques. The video inpainting is achieved by dividing the video into frames and the motion of the moving objects in the frames are tracked by applying the motion tracking method. The existing inpainting method proposed by the Criminisi, neglected the local similarities in the images so it suffered from dropping effect in the priority computation. This paper proposed a new priority computation method by introducing gradient operation with the addition of curvature in the data term and local structure measurement function with structure tensor theory as an additional term. Later, the patch matching is achieved with the Sum of Absolute Difference (SAD) distance method. Further, the optimal patch is selected by applying the Grey Wolf Optimization (GWO) algorithm. The efficiency of the proposed video inpainting technique is evaluated with the performance metrics, viz., Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Edge Similarity (ESIM) executed in MATLAB. The PSNR and SSIM of the proposed method for Fontaine_chatelet video is improved by 18.9% and 4.19% than existing method. The proposed method is compared with other existing methods also and it outperformed the existing methods.