Deep neural network with deformable convolution and side window convolution for image denoising

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A noval neural networks with irregular convolution block is proposed for image denoising. In the field of image processing, convolutional neural networks have shown great advantages compared with traditional approaches, however, it is found that standard convolution does not work well on image edge, and it has some drawbacks when dealing with variable noised images. In this paper, we numerically illustrate that the irregular convolution, including deformable convolutional kernel and side window filtering technique, is beneficial for finding effective receptive field and improving image edge. Quantitative and qualitative experimental results are demonstrated, which outperforms classical convolution neural networks in denoising tasks.