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

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.