Title: PNEN: Pyramid Non-Local Enhanced Networks. Abstract: Existing neural networks proposed for low-level image processing tasks are usually implemented by stacking convolution layers with limited kernel size. Every convolution layer merely involves in context information from a small local neighborhood. More contextual features can be explored as more convolution layers are adopted. However it is difficult and costly to take full advantage of long-range dependencies. We employ non-local operation to build up connection between every pixel and all remain pixels. Moreover a novel \emph{Pyramid Non-local Block} is devised to robustly estimate pairwise similarity coefficients between different scales of content patterns. Considering computation burden and memory consumption, we exploit embedding feature maps with coarser resolution to represent content patterns with larger spatial scale. Through elaborately combining the pyramid non-local blocks and dilated residual blocks, we set up a \emph{Pyramid Non-local Enhanced Network} for edge-preserving image smoothing. It achieves state-of-the-art performance in imitating three classical image smoothing algorithms. Additionally, the pyramid non-local block can be directly incorporated into existing convolution neural networks for other image processing tasks. We integrate it into two state-of-the-art methods for image denoising and single image super-resolution respectively, achieving consistently improved performance.