Hybridized Cuckoo Search with Multi-Verse Optimization-Based Patch Matching and Deep Learning Concept for Enhancing Video Inpainting

This paper aims to develop a novel deep learning concept to deal with video inpainting. Initially, motion tracking is performed, which is the process of determining motion vectors that describe the transformation from adjacent frames in a video sequence. Further, the regions or patches of each frame are categorized using the optimized recurrent neural network (RNN), in which the region is split into a smooth and structure region. It is performed using the texture feature called grey-level co-occurrence matrix. The filling of the smooth region is accomplished by replacing with the mean pixels of unmasked region, and the structure region is done by optimized patch matching approach based on scale-invariant feature transform (SIFT). The main objective optimized patch matching is based on the minimized Euclidean distance between the extracted SIFT features of the original patch and reference patch, and the certain patch is inpainted by the optimized patch. Here, the hybridization of two meta-heuristic algorithms like cuckoo search algorithm and multi-verse optimization (MVO) called Cuckoo Search-based MVO is used to optimize the RNN and patch matching. Finally, the experimental results verify the reliability of the proposed algorithm over existing algorithms.