Single image super-resolution based on directional variance attention network

Highlights：

• We propose a lightweight and efficient directional variance attention network (DiVANet) for high-quality image SR-. Extensive experiments on a variety of public datasets demonstrate the superiority of the proposed architecture over state-of-the-art models.

• We propose a directional variance attention mechanism (DiVA), to enhance features in different channels and spatial regions. Such a mechanism allows the network to focus on more informative features.

• We introduce a novel procedure for using attention mechanisms together with residual blocks, following two independent but parallel computational paths in order to facilitate the preservation of finer details.