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An optimized profound memory-affiliated de-noising of aerial images through deep neural network for disaster management

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Abstract

De-noising is an effective mechanism for removing the aberration present in the image and has been exploited in diverse fields. In this proposed research work, a novel deep learning-based profound memory-affiliated neural network (PMANN) for de-noising aerial images with disaster management is implemented. The proposed method is optimized with an adaptive dual-threshold wavelet transform for precise noise suppression of aerial images. This proposed architecture overrides the prior art methods employed for de-noising as well as disaster management. The intended scheme is correlated over the preexisting noise removal techniques such as convolution neural network (CNN), CNN with long short-time memory (CNN-LSTM), weighted nuclear norm minimization (WNNM), and de-noising CNN (DNCNN), respectively. The peak signal–noise ratio value of the proposed PMANN is increased by 0.24%, 0.086%, 0.643%, and 0.720% compared to CNN, CNN-LSTM, WNNM, and DNCNN models, respectively. The structural similarity index value is increased by 0.59%, 0.382%, 0.037%, and 0.465% compared to CNN, CNN-LSTM, WNNM, and DNCNN techniques. The mean-squared error value is decreased by 8.93%, 2.1457%, 0.316%, and 0.582% compared to CNN, CNN-LSTM, WNNM, and DNCNN techniques, respectively.

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Acknowledgements

The authors would like to thank the reviewers for all of their careful, constructive, and insightful comments in relation to this work.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, PSN College of Engineering and Technology, Tirunelveli, India

    T. Ajith Bosco Raj & A. Ahilan

  2. Department of Computer Science and Engineering, Arunachala College of Engineering for Women, Kanyakumari, India

    C. Pushpalatha

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  3. A. Ahilan
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Contributions

The authors confirm contribution to the paper as follows: study conception and design was performed by TABR, CPL, and AA; data collection was done by TABR and CPL; analysis and interpretation of results were done by AA and CPL; draft manuscript preparation was performed by TABR, CPL, and AA. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to T. Ajith Bosco Raj or C. Pushpalatha.

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Raj, T.A.B., Pushpalatha, C. & Ahilan, A. An optimized profound memory-affiliated de-noising of aerial images through deep neural network for disaster management. SIViP 17, 3983–3991 (2023). https://doi.org/10.1007/s11760-023-02628-6

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