Volume 13, Issue 3

Voice Vista


Dr. A. Meenakshi1, Bava Dharani B2, Mariammal D3, Latha K4


Blind image restoration involves leveraging prior information within an image to restore the sharpness of edges. De-blurring, on the other hand, aims to eliminate blurring artifacts caused by factors like defocus aberration or motion blur, which manifests as apparent streaking in still images of rapidly moving objects. Gaussian blur results from applying a Gaussian function to blur an image. Recent advancements in single-image methods owe their success in part to the utilization of various sparse priors for either latent images or motion blur kernels. KSR introduces an efficient kernel matrix approximation to accelerate blurring processes and achieve notable de-blur performance on digital datasets. License plate recognition serves as a critical tool for identifying over-speed vehicles or those involved in hit-and-run incidents. However, surveillance camera snapshots of speeding vehicles often suffer from motion blur, rendering them unrecognizable to the human eye and presenting a challenge to existing blind deblurring techniques. To address this, we propose a novel sparse representation-based scheme for identifying motion blur kernels. By analyzing sparse representation coefficients of the recovered image, we determine the kernel angle, and estimate the length of the motion kernel using Radon transform in Fourier domain. Our approach effectively handles large motion blur, even when license plates are unidentifiable by humans. Experimental evaluations on real-world images demonstrate the superiority of our method over several state-of-the-art blind image de-blurring algorithms.



PAGES : 1297-1300 | 5 VIEWS | 3 DOWNLOADS

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Dr. A. Meenakshi1, Bava Dharani B2, Mariammal D3, Latha K4 | Voice Vista | DOI : https://doi.org/10.62226/ijarst20241336

Journal Frequency: ISSN 2320-1126, Monthly
Paper Submission: Throughout the month
Acceptance Notification: Within 6 days
Subject Areas: Engineering, Science & Technology
Publishing Model: Open Access
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Publication Impact Factor: 6.76
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