基于空频域协同学习的轻量级红外偏振图像质量提升方法

王秉路; 武剑珍; 汪顺舟

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基于空频域协同学习的轻量级红外偏振图像质量提升方法

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- 基于空频域协同学习的轻量级红外偏振图像质量提升方法
- Lightweight infrared polarization image enhancement method via spatial-frequency collaborative learning
- 物联网学报 2026年
- 作者机构：
  
  1.西安建筑科技大学信息与控制工程学院，陕西西安 710399
  2.西北工业大学航天学院，陕西西安 710129
  3.河南大学人工智能学院，河南郑州 450046
- 作者简介：
  
  王秉路（1992年11月），男，2021年获西北工业大学自动化学院控制科学与工程博士学位。2022年至2024年，任北京理工大学信息与电子学院博士后研究员。现为西北工业大学航天学院教授、西安建筑科技大学兼职教授。研究方向包括计算机视觉、数字信号处理与深度学习。
  武剑珍（2001年8月），女，现就读于西安建筑科技大学计算机科学与技术专业，攻读硕士学位。研究方向包括图像超分辨率与图像重建。
  汪顺舟（1992年10月），男，2023年在北京理工大学获博士学位，现为河南大学人工智能学院讲师，硕士生导师，研究方向包括光场图像超分辨率与视图合成。
- 基金信息：
  
  国家自然科学基金项目(62401447);陕西省重点研发计划项目(2024GX-YBXM-051);陕西省自然科学基础研究计划资助项目(2025JC-YBQN-877);河南省科技攻关项目(262102210162)
- DOI：
  中图分类号：
- 收稿：2026-01-26，
  
  修回：2026-03-25，
  
  录用：2026-03-25，
- 稿件说明：
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王秉路, 武剑珍, 汪顺舟. 基于空频域协同学习的轻量级红外偏振图像质量提升方法[J/OL]. 物联网学报, 2026.

WANG Binglu, WU Jianzhen, WANG Shunzhou. Lightweight infrared polarization image enhancement method via spatial-frequency collaborative learning[J/OL]. Chinese Journal on Internet of Things, 2026.
王秉路, 武剑珍, 汪顺舟. 基于空频域协同学习的轻量级红外偏振图像质量提升方法[J/OL]. 物联网学报, 2026. DOI：

WANG Binglu, WU Jianzhen, WANG Shunzhou. Lightweight infrared polarization image enhancement method via spatial-frequency collaborative learning[J/OL]. Chinese Journal on Internet of Things, 2026. DOI：

摘要

复杂背景的目标识别是物联网边缘感知体系中很重要的任务，而红外偏振图像在安防，伪装检测领域展现出优越的目标背景对比度。然而，由于成像系统计算速度慢以及传感器硬件缺陷问题，长波红外焦平面分割偏振成像系统在实际应用中难以高效获取高质量的图像，阻碍下游检测等任务的精度。为此，提出一种基于空频域协同的轻量级红外偏振图像联合去噪与去马赛克方法，该方法通过构建三阶段学习网络，在保证低计算复杂度的同时提升偏振图像重建质量。所设计的双域交互去噪模块充分利用频域信息的统计性特征，有效抑制噪声并保留偏振特性；同时，在粗糙去马赛克之后引入轻量化精细重建模块，通过深度分组卷积压缩模型参数量并提升重建性能。实验结果表明，所提方法在 IR-DoT 与 IR-DoFP 数据集上在保持低计算开销的前提下取得了优于现有方法的重建效果，为物联网边缘终端提供了一种低功耗、高质量的感知数据预处理方案。

Abstract

Object recognition in complex scenes is an important task in the Internet of Things (IoT) based edge perception systems. Infrared polarization images show superior foreground–background contrast in security monitoring and camouflage detection. However

due to the limited hardware resources of imaging systems

it is difficult for long-wave infrared division-of-focal-plane (DoFP) polarization imaging systems to achieve high-quality images in practical applications

which adversely affects edge perception performance. To this end

a lightweight joint denoising and demosaicking network for infrared polarization images is developed via spatial-frequency collaborative learning. The proposed method employs a three-stage learning network to enhance the quality of polarization image reconstruction while maintaining low computational complexity. The dual-domain interactive denoising block leverages the statistical properties of the frequency domain to suppress noise while preserving polarization features. After coarse demosaicking

a lightweight fine reconstruction module is adopted to generate the final results. Depthwise grouped convolutions are used to reduce model parameter count and improve reconstruction quality. Extensive experiments are performed on the IR-DoT and IR-DoFP datasets. The results show that the proposed method achieves superior performance compared with other leading approaches while maintaining low computational overhead

making it a low-power

high-quality data preprocessing approach for IoT-based edge devices.

关键词

Keywords

references

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