# Lightweight Feature Fusion Network for Single Image Super-Resolution

## TL;DR

This paper introduces a lightweight neural network architecture for single image super-resolution that efficiently explores multi-scale features, reduces parameters, and achieves competitive results compared to state-of-the-art methods.

## Contribution

The proposed LFFN utilizes spindle blocks and a softmax feature fusion module to enhance feature exploration while significantly reducing model parameters.

## Key findings

- LFFN outperforms comparable models on benchmark datasets.
- LFFN achieves similar or better super-resolution quality with fewer parameters.
- Qualitative results show improved visual details.

## Abstract

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.