Deep convolutional neural networks (CNNs) have achieved considerable success in terms of image denoising. However, previous CNN denoisers have been restricted by rigid kernel convolution that applies equal spatial treatment across images. To fully utilize the local differences, we propose a kernel prediction network that examines each pixel region and predicts unique pixel-wise kernels. Several optimizations have been further designed to gather sufficient information for single-image denoising task. We adopt dilated residual blocks to view the local pixel region at varying receptive fields. Then, kernel fusion assembles the information from different scopes and generates accurate kernels for each pixel. Instead of applying the predicted kernels to the original image, we construct a compressed feature map as a substitution such that more relevant local features are collected. Experiments are used to demonstrate that our network achieves favorable results compared with state-of-the-art methods and is adequate for practical applications.

中文翻译：

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用于单图像去噪的膨胀核预测网络

深度卷积神经网络（CNN）在图像去噪方面已经取得了相当大的成功。但是，以前的CNN去噪器受到刚性核卷积的限制，该刚性核卷积在整个图像上应用相同的空间处理。为了充分利用局部差异，我们提出了一个内核预测网络，该网络可以检查每个像素区域并预测唯一的像素级内核。进一步优化了一些优化措施，以收集足够的信息来处理单幅图像降噪任务。我们采用膨胀的残差块来查看变化的接收场处的局部像素区域。然后，核融合会收集来自不同范围的信息，并为每个像素生成准确的核。与其将预测的核应用于原始图像，我们构建了一个压缩的特征图作为替代，以便收集更多相关的局部特征。实验用来证明我们的网络与最先进的方法相比取得了令人满意的结果，并且足以用于实际应用。