  Transformer-based deep models for single image super-resolution (SISR) have
greatly improved the performance of lightweight SISR tasks in recent years.
However, they often suffer from heavy computational burden and slow inference
due to the complex calculation of multi-head self-attention (MSA), seriously
hindering their practical application and deployment. In this work, we present
an efficient SR model to mitigate the dilemma between model efficiency and SR
performance, which is dubbed Entropy Attention and Receptive Field Augmentation
network (EARFA), and composed of a novel entropy attention (EA) and a shifting
large kernel attention (SLKA). From the perspective of information theory, EA
increases the entropy of intermediate features conditioned on a Gaussian
distribution, providing more informative input for subsequent reasoning. On the
other hand, SLKA extends the receptive field of SR models with the assistance
of channel shifting, which also favors to boost the diversity of hierarchical
features. Since the implementation of EA and SLKA does not involve complex
computations (such as extensive matrix multiplications), the proposed method
can achieve faster nonlinear inference than Transformer-based SR models while
maintaining better SR performance. Extensive experiments show that the proposed
model can significantly reduce the delay of model inference while achieving the
SR performance comparable with other advanced models.
