Nature-Inspired DBN based Optimization Techniques for Image De-noising

Abstract

Whale optimization algorithm (WOA) and particle swarm optimization (PSO) are heuristic techniques used to solve various engineering optimization problems. In this paper, these algorithms have been used in combination with a relatively less explored deep-learning model, viz., deep belief network (DBN) for Gaussian de-noising. DBNs are stacked restricted Boltzmann machines (RBMs) whose typical architectural characteristics make deep learning feasible by reducing the training complexity. The de-noising results of images corrupted by additive white Gaussian noise (AWGN) using three proposed networks; MWOA-DBN, WOA-DBN, and PSO-DBN are provided. Super parameters (step ratio and dropout rate) are optimized using MWOA, WOA, and PSO with root mean square error as the fitness function to circumvent over-fitting. The nature of convergence of the fitness function is tested for variation in step ratio, and dropout rate. The performance of the de-noising method is tested on bench-mark metrics like peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root-mean-square error (RMSE). It is observed that the performance of the proposed methods outperforms the state-of-the-art image de-noising techniques.