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New image denoising algorithm using monogenic wavelet transform and improved deep convolutional neural network
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DOI：10.1007/s11042-019-08569-y.png)
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Abstract
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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.
Keywords:
Image de-noising
Monogenic wavelet transform
Neural network
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