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Median filters combined with denoising convolutional neural network for Gaussian and impulse noises

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Abstract

Elimination of combined Gaussian and impulse noises in digital image processing with preservation of image details and suppression of noise are challenging problem. For this purpose, a new filter which is median filters combined with convolutional neural network for Gaussian and salt & pepper noises. The previous methods are application dependents; some used for impulse noise and other employed only for Gaussian noise. The elimination of Gaussian and impulse noise completed into two steps. First the detection of impulse noise with the rejection of noise by employed of 3 × 3 and 5 × 5 window size median filters. In the second step removal of Gaussian noise performed by residual learning denoising convolutional neural network. It is very favorable and the ability of learning and denoising performance in the field of digital image processing. Denoising convolutional neural network also has active Gaussian noise with an unknown level of noise. Experimental work showed that the proposed method can achieve low loss and root mean square error during training, high peak signal to noise ratio, low mean square error, image quality assessment with good quality and mean absolute error for close prediction between denoised and original color images.

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Acknowledgments

This Paper is supported by the National Natural Science Foundation of China, China [Grand number:61671185].

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Authors and Affiliations

  1. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Alam Noor, Yaqin Zhao, Rahim Khan, Longwen Wu & Fakheraldin Y.O. Abdalla

  2. Robotics & Internet-of-Things Lab, Prince Sultan University, Riyadh, Saudi Arabia

    Alam Noor

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  1. Alam Noor
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  2. Yaqin Zhao
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  3. Rahim Khan
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  4. Longwen Wu
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  5. Fakheraldin Y.O. Abdalla
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Correspondence to Yaqin Zhao.

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Noor, A., Zhao, Y., Khan, R. et al. Median filters combined with denoising convolutional neural network for Gaussian and impulse noises. Multimed Tools Appl 79, 18553–18568 (2020). https://doi.org/10.1007/s11042-020-08657-4

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  • DOI: https://doi.org/10.1007/s11042-020-08657-4

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