Smartphone video motion deblur order model
https://doi.org/10.17586/2226-1494-2024-24-3-483-489
Abstract
A method has been proposed to eliminate slight motion blur in the image. The method is implemented in three stages. Blur estimation is achieved by prior information on the distribution image gradient. The Gaussian Orientation Filter (GOF) fits the prior information to find the regression coefficients. Order combines different estimate GOF parameters to generate a removal blur filter. Estimation parameters are fixed and set blur on the image to produce an image without boosting the noise and unwanted. The proposed model optimization solves the problem by minimizing the loss function. The suggested method applies to outdoor and indoor video acquired by modern smartphones. The experiment result display is accurate for the full regression motion blur model. The suggested model example on video dataset conditions has 23 s video time long and 228 MP dataset size. Measurement evaluation established on time consumer, Structural Similarity Index Measure and Peak Signal-to-Noise Ratio. Experimental results show that the image artifact phase is less consuming computational time. The proposed model has a minimized cost function and generates image quality.
About the Author
R. А. Sallama
Directorate General of Vocational Education — Vocational Edu Iraq
Iraq
Iraq
Resen Adhab Sallama — PhD, Lecturer
Bagdad, 10001
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Review
For citations:
Sallama R.А. Smartphone video motion deblur order model. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2024;24(3):483-489. https://doi.org/10.17586/2226-1494-2024-24-3-483-489
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