EPNet: An Efficient Pyramid Network for Enhanced Single-Image
  Super-Resolution with Reduced Computational Requirements
Xin Xu Jinman Park Paul Fieguth
Single-image super-resolution (SISR) has seen significant advancements
through the integration of deep learning. However, the substantial
computational and memory requirements of existing methods often limit their
practical application. This paper introduces a new Efficient Pyramid Network
(EPNet) that harmoniously merges an Edge Split Pyramid Module (ESPM) with a
Panoramic Feature Extraction Module (PFEM) to overcome the limitations of
existing methods, particularly in terms of computational efficiency. The ESPM
applies a pyramid-based channel separation strategy, boosting feature
extraction while maintaining computational efficiency. The PFEM, a novel fusion
of CNN and Transformer structures, enables the concurrent extraction of local
and global features, thereby providing a panoramic view of the image landscape.
Our architecture integrates the PFEM in a manner that facilitates the
streamlined exchange of feature information and allows for the further
refinement of image texture details. Experimental results indicate that our
model outperforms existing state-of-the-art methods in image resolution
quality, while considerably decreasing computational and memory costs. This
research contributes to the ongoing evolution of efficient and practical SISR
methodologies, bearing broader implications for the field of computer vision.