Open Access

ARTICLE

A Lightweight Super-Resolution Network for Infrared Images Based on an Adaptive Attention Mechanism

Mengke Tang¹, Yong Gan^2,*, Yifan Zhang¹, Xinxin Gan³

1 School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450001, China
2 School of Information Engineering, Zhengzhou University of Technology, Zhengzhou, 450044, China
3 Digital and Intelligent Engineering Design Institute, SIPPR Engineering Group Co., Ltd., Zhengzhou, 450007, China

* Corresponding Author: Yong Gan. Email:

Computers, Materials & Continua 2025, 84(2), 2699-2716. https://doi.org/10.32604/cmc.2025.064541

Received 18 February 2025; Accepted 27 April 2025; Issue published 03 July 2025

Abstract

Infrared imaging technology has been widely adopted in various fields, such as military reconnaissance, medical diagnosis, and security monitoring, due to its excellent ability to penetrate smoke and fog. However, the prevalent low resolution of infrared images severely limits the accurate interpretation of their contents. In addition, deploying super-resolution models on resource-constrained devices faces significant challenges. To address these issues, this study proposes a lightweight super-resolution network for infrared images based on an adaptive attention mechanism. The network’s dynamic weighting module automatically adjusts the weights of the attention and non-attention branch outputs based on the network’s characteristics at different levels. Among them, the attention branch is further subdivided into pixel attention and brightness-texture attention, which are specialized for extracting the most informative features in infrared images. Meanwhile, the non-attention branch supplements the extraction of those neglected features to enhance the comprehensiveness of the features. Through ablation experiments, we verify the effectiveness of the proposed module. Finally, through experiments on two datasets, FLIR and Thermal101, qualitative and quantitative results demonstrate that the model can effectively recover high-frequency details of infrared images and significantly improve image resolution. In detail, compared with the suboptimal method, we have reduced the number of parameters by 30% and improved the model performance. When the scale factor is 2, the peak signal-to-noise ratio of the test datasets FLIR and Thermal101 is improved by 0.09 and 0.15 dB, respectively. When the scale factor is 4, it is improved by 0.05 and 0.09 dB, respectively. In addition, due to the lightweight design of the network structure, it has a low computational cost. It is suitable for deployment on edge devices, thus effectively enhancing the sensing performance of infrared imaging devices.

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Keywords

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