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New image denoising algorithm using monogenic wavelet transform and improved deep convolutional neural network

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Abstract

The new image de-nosing algorithm based on improved deep convolutional neural network in the monogenic wavelet domain is proposed in this paper. The monogenic wavelet transform was employed to describe the amplitude and phase information of the noisy image. Then, the amplitude and phase information are simultaneously used as input of proposed improved convolutional neural network for denoising. Finally, the monogenic wavelet inverse transform is used to obtain the denoised image. The experimental results illustrate that the proposed algorithm achieves superior performance both in visual quality and objective peak signal-to-noise ratio values, compared with other state-of-the-art de-noising algorithms.

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Acknowledgements

This work is partially supported by National Natural Science Foundation of China; the grant number is 61563037, 61866027; Outstanding Youth Scheme of Jiangxi Province; the grant number is 20171BCB23057; Key research project of Jiangxi Province under grant 20171BBE50013; The Jiangxi Science Fund for Distinguished Young Scholars under grand 20192ACB21032, Key research project of Jiangxi Province under grant 20171BBE50013.

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Correspondence to Shan Gai.

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Bao, Z., Zhang, G., Xiong, B. et al. New image denoising algorithm using monogenic wavelet transform and improved deep convolutional neural network. Multimed Tools Appl 79, 7401–7412 (2020). https://doi.org/10.1007/s11042-019-08569-y

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