  Recently, deep convolutional neural networks (CNNs) have been widely explored
in single image super-resolution (SISR) and contribute remarkable progress.
However, most of the existing CNNs-based SISR methods do not adequately explore
contextual information in the feature extraction stage and pay little attention
to the final high-resolution (HR) image reconstruction step, hence hindering
the desired SR performance. To address the above two issues, in this paper, we
propose a two-stage attentive network (TSAN) for accurate SISR in a
coarse-to-fine manner. Specifically, we design a novel multi-context attentive
block (MCAB) to make the network focus on more informative contextual features.
Moreover, we present an essential refined attention block (RAB) which could
explore useful cues in HR space for reconstructing fine-detailed HR image.
Extensive evaluations on four benchmark datasets demonstrate the efficacy of
our proposed TSAN in terms of quantitative metrics and visual effects. Code is
available at https://github.com/Jee-King/TSAN.
