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Profiling Maternal Behavior Responses During Whole-Brain Imaging

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

M Nuthal Srinivasan¹, M Chinnadurai², S Senthilkumar³

¹Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, 611002, India. nuthal4u@gmail.com.

Scientific Reports

|July 5, 2024

Summary

This study introduces a new video inpainting method, MHWT-KHCA, to efficiently fill missing video areas without visible seams. The novel technique significantly improves computational speed and visual quality in video restoration.

Keywords:

Criminisi algorithm Down sampling Haar wavelet Krill herd optimization Video inpainting Wavelet decomposition

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Area of Science:

Computer Vision
Digital Image Processing

Background:

Current video inpainting methods struggle with high computation complexity and visible seam artifacts due to variations in brightness and patches.
Existing techniques often fail to produce seamless results in the target areas.

Purpose of the Study:

To introduce a novel video inpainting technique that addresses the challenges of high computational demand and visible seam artifacts.
To improve the efficiency and seamlessness of video inpainting processes.

Main Methods:

The proposed technique combines the Morphological Haar Wavelet Transform (MHWT) with the Krill Herd based Criminisi algorithm (KHCA).
MHWT-KHCA is designed to strategically reduce computation times and enhance seamlessness.

Main Results:

Experimental validation using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics.
The MHWT-KHCA technique demonstrated superior performance compared to existing video inpainting methods.

Conclusions:

The novel MHWT-KHCA algorithm effectively reduces computational complexity and eliminates visible seam artifacts in video inpainting.
The technique shows promise for real-world applications like video restoration and surveillance enhancement.