Title: DRN-MCOA: image deblurring using deep residual network with modified coot optimisation algorithm

Authors: Godekere Shivashankar Yogananda; Ananda Babu Jayachandra; Ahmad Alkhayyat; Dayananda Pruthviraja

Addresses: Department of Information Science and Engineering, Malnad College of Engineering, Hassan, Karnataka, India ' Department of Information Science and Engineering, Malnad College of Engineering, Hassan, Karnataka, India ' Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq; Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Al-Qādisiyyah, Iraq ' Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India

Abstract: In this manuscript, a hybrid model is introduced for effective image deblurring. A Deep Residual Network (DRN) is implemented for reducing artificial traces in the patches, which results in pleasant denoised images. Secondly, a Modified Coot Optimisation Algorithm (MCOA) is incorporated with the DRN for selecting optimal kernel and threshold parameters. The exploitation and exploration abilities of the MCOA are improved by employing an opposition-based learning method and Cauchy mutation. This process resolves the problem of local optima and improves the convergence rate. This DRN-MCOA model's efficacy is investigated on real-time images and the RealBlur data set. The DRN-MCOA model obtained a Peak Signal to Noise Ratio (PSNR) of 33.40 dB and a Structural Similarity Index (SSIM) of 0.96 on a real-time collected image. Correspondingly, it achieved a PSNR of 30.34 dB and 37.55 dB and an SSIM of 0.92 and 0.96 on the RealBlur-J and RealBlur-R data sets.

Keywords: coot optimisation algorithm; deep residual network; image deblurring; image processing; restoration.

DOI: 10.1504/IJCAT.2025.149362

International Journal of Computer Applications in Technology, 2025 Vol.76 No.3/4, pp.194 - 206

Received: 08 Jul 2024
Accepted: 20 Mar 2025
Published online: 27 Oct 2025 *

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