Multiobjective Reptile Search Algorithm Based Effective Image Deblurring and Restoration

Abstract

Images are frequently affected because of blurring, and data loss occurred by sampling and noise occurrence. The images are getting blurred because of object movement in the scenario, atmospheric misrepresentations, and optical aberrations. The main objective of image restoration is to evaluate the original image from the corrupted data. To overcome this issue, the multiobjective reptile search algorithm is proposed for performing an effective image deblurring and restoration (MORSA-IDR). The proposed MORSA is used in two different processes such as threshold and kernel parameter calculation. In that, threshold values are used for detecting and replacing the noisy pixel removal using deep residual network, and estimation of kernel is performed for deblurring the images. The main objective of the proposed MORSA-IDR is to enhance the process of deblurring for recovering low-level contextual information. The MORSA-IDR is evaluated using peak signal noise ratio (PSNR) and structural similarity index. The existing researches such as enhanced local maximum intensity (ELMI) prior and deep unrolling for blind deblurring (DUBLID) are used to evaluate the MORSA-IDR. The PSNR of MORSA-IDR for image 6 is 30.98 dB, which is high when compared with the ELMI and DUBLID.