  Single image super-resolution(SISR) has witnessed great progress as
convolutional neural network(CNN) gets deeper and wider. However, enormous
parameters hinder its application to real world problems. In this letter, We
propose a lightweight feature fusion network (LFFN) that can fully explore
multi-scale contextual information and greatly reduce network parameters while
maximizing SISR results. LFFN is built on spindle blocks and a softmax feature
fusion module (SFFM). Specifically, a spindle block is composed of a dimension
extension unit, a feature exploration unit and a feature refinement unit. The
dimension extension layer expands low dimension to high dimension and
implicitly learns the feature maps which is suitable for the next unit. The
feature exploration unit performs linear and nonlinear feature exploration
aimed at different feature maps. The feature refinement layer is used to fuse
and refine features. SFFM fuses the features from different modules in a
self-adaptive learning manner with softmax function, making full use of
hierarchical information with a small amount of parameter cost. Both
qualitative and quantitative experiments on benchmark datasets show that LFFN
achieves favorable performance against state-of-the-art methods with similar
parameters.
