Nature-Inspired DBN based Optimization Techniques for Image De-noising
In: Intelligent Systems with Applications, Jg. 18 (2023-05-01), Heft 200211-
Online
academicJournal
Zugriff:
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.
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Nature-Inspired DBN based Optimization Techniques for Image De-noising
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Autor/in / Beteiligte Person: | Rini Smita Thakur ; Chatterjee, Shubhojeet ; Ram Narayan Yadav ; Gupta, Lalita |
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Zeitschrift: | Intelligent Systems with Applications, Jg. 18 (2023-05-01), Heft 200211- |
Veröffentlichung: | Elsevier, 2023 |
Medientyp: | academicJournal |
ISSN: | 2667-3053 (print) |
DOI: | 10.1016/j.iswa.2023.200211 |
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