Weakly illuminated image enhancement as a kind of pre-processing technologies is widely used in various computer vision tasks. Traditional image enhancement methods has poor robustness. While, methods based on existing convolutional neural networks (CNNs) estimate the enhanced image from weakly illuminated image directly without injecting the visual attention mechanism, ignoring weakly illuminated regions and leading to inaccuracy result. To resolve this problem, we propose an attentive residual dense network for weakly illuminated image enhancement. The proposed network contains two parts: attentive recurrent network and residual dense network. With the guidance of the illumination map, attentive recurrent network focuses more and more on the weakly illuminated regions and generates the attentive illumination map following a coarse-to-fine strategy via the recurrent architecture. This attentive illumination map concatenated with the weakly illuminated image are injected into the subsequent residual dense network to ensure this network assign more computational source to weakly illuminated regions and estimate enhanced image accurately. The experiments demonstrate that our method achieves favorable performance against that of existing image enhancement methods based on synthetic images and real images.