Gradient Prior-Aided CNN Denoiser With Separable Convolution-Based Optimization of Feature Dimension

초록

We propose a novel image denoising method based on a convolutional neural network (CNN), which uses the separable convolution and the gradient prior to reduce the computational complexity while enhancing the denoising performance. The proposed method converts the existing convolution filter in the conventional CNN denoiser to cascaded vertical and horizontal separable convolutions and reduces the number of feature channels between these convolutions by analyzing the distribution of convolution weights. The proposed separable convolution with feature dimension shrinking can greatly reduce the number of multiplications for CNN while minimizing the degradation of denoising quality. In addition, gradients of a given image are used as input for the proposed CNN denoiser by exploiting the relation between an anisotropic diffusion-based denoiser and a residual CNN denoiser to improve the quality of the image denoising. The simulation results showed that the proposed method provided comparable denoising quality while reducing the number of multiplications to 41% compared to the existing state-of-the-art CNN denoiser.

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