| Autor: |
Rini Smita Thakur, Shubhojeet Chatterjee, Ram Narayan Yadav, Lalita Gupta |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Intelligent Systems with Applications, Vol 18, Iss , Pp 200211- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2667-3053 |
| DOI: |
10.1016/j.iswa.2023.200211 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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