An effective video inpainting technique using morphological Haar wavelet transform with krill herd based criminisi algorithm.

Autor: Srinivasan MN; Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, 611002, India. nuthal4u@gmail.com., Chinnadurai M; Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, 611002, India., Senthilkumar S; Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, 611002, India., Dinesh E; Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, 639113, India.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Jul 05; Vol. 14 (1), pp. 15485. Date of Electronic Publication: 2024 Jul 05.
DOI: 10.1038/s41598-024-66496-x
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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