Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network

TL;DR

This paper introduces a lightweight multi-scale feature interaction network (MSFIN) for single image super-resolution that balances high performance with low memory usage, making it suitable for mobile devices.

Contribution

The paper proposes a novel lightweight network architecture with multi-scale feature interaction and a recurrent residual channel attention block for efficient super-resolution.

Findings

MSFIN achieves comparable results to state-of-the-art methods.

The model is more lightweight and suitable for mobile devices.

Extensive experiments validate the effectiveness of MSFIN.

Abstract

Recently, the single image super-resolution (SISR) approaches with deep and complex convolutional neural network structures have achieved promising performance. However, those methods improve the performance at the cost of higher memory consumption, which is difficult to be applied for some mobile devices with limited storage and computing resources. To solve this problem, we present a lightweight multi-scale feature interaction network (MSFIN). For lightweight SISR, MSFIN expands the receptive field and adequately exploits the informative features of the low-resolution observed images from various scales and interactive connections. In addition, we design a lightweight recurrent residual channel attention block (RRCAB) so that the network can benefit from the channel attention mechanism while being sufficiently lightweight. Extensive experiments on some benchmarks have confirmed that our proposed MSFIN can achieve comparable performance against the state-of-the-arts with a more lightweight model.