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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.
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Details
1 E.G.S. Pillay Engineering College, Department of Electronics and Communication Engineering, Nagapattinam, India (ISNI:0000 0004 5939 3224)
2 E.G.S. Pillay Engineering College, Department of Computer Science and Engineering, Nagapattinam, India (ISNI:0000 0004 5939 3224)
3 M. Kumarasamy College of Engineering, Department of Electronics and Communication Engineering, Karur, India (ISNI:0000 0004 1774 2107)