Abstract

In recent times, video inpainting techniques have intended to fill the missing areas or gaps in a video by utilizing known pixels. The variety in brightness or difference of the patches causes the state-of-the-art video inpainting techniques to exhibit high computation complexity and create seams in the target areas. To resolve these issues, this paper introduces a novel video inpainting technique that employs the Morphological Haar Wavelet Transform combined with the Krill Herd based Criminisi algorithm (MHWT-KHCA) to address the challenges of high computational demand and visible seam artifacts in current inpainting practices. The proposed MHWT-KHCA algorithm strategically reduces computation times and enhances the seamlessness of the inpainting process in videos. Through a series of experiments, the technique is validated against standard metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), where it demonstrates superior performance compared to existing methods. Additionally, the paper outlines potential real-world applications ranging from video restoration to real-time surveillance enhancement, highlighting the technique’s versatility and effectiveness. Future research directions include optimizing the algorithm for diverse video formats and integrating machine learning models to advance its capabilities further.

Details

Title
An effective video inpainting technique using morphological Haar wavelet transform with krill herd based criminisi algorithm
Author
Srinivasan, M. Nuthal 1 ; Chinnadurai, M. 2 ; Senthilkumar, S. 1 ; Dinesh, E. 3 

 E.G.S. Pillay Engineering College, Department of Electronics and Communication Engineering, Nagapattinam, India (ISNI:0000 0004 5939 3224) 
 E.G.S. Pillay Engineering College, Department of Computer Science and Engineering, Nagapattinam, India (ISNI:0000 0004 5939 3224) 
 M. Kumarasamy College of Engineering, Department of Electronics and Communication Engineering, Karur, India (ISNI:0000 0004 1774 2107) 
Pages
15485
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3076105082
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.