Reinforced black widow algorithm with restoration technique based on optimized deep generative adversarial network

Abstract

ABSTRACT Image restoration is used to develop the quality of image that is triggered by various noises and blurring. During this causes, certain areas in the images are vanished. The existing works does not provide sufficient restoration process with high accuracy. Therefore, a new image restoration system based on Optimized Deep Generative Adversarial Network (DGAN) with Reinforced Black Widow algorithm (BWOA) is proposed in this paper to increase the restoration accuracy and reducing the noises. At first, the input image is converted as gray scale image and the multi-scale edge information is removed as damaged area of an image by constructing a smooth function. Here, the extracted multi-scale edge information is given to the DGAN model. After that, the images are trained to create the best fake images through continuous play among generator and discriminator. Then, the detected images are restored in the original image with high accuracy. The hyper parameters of the DGAN are optimized by using the BWOA. The major objective of this paper is ‘to increase the restoration accuracy and the quality of the image by decreasing the noises occurred in the input image.’ The simulation process is performed on the MATLAB platform. The proposed DGAN-BWOA-IR attains higher restoration accuracy of 9.3%, higher PSNR value 74.589(db), SSIM 9.023% the proposed system is likened with the existing approaches, such as plug-and-play image restoration including deep denoiser prior (DCNN-IR), Learning enriched features for rapid image restoration with enhancement (LEF-IR), Exploiting deep generative prior for versatile image restoration with manipulation (GAN-IR), respectively.