Polarization Image Super-Resolution Reconstruction Network Using Contextual Dual Self-Correlation Attention

CHEN Guangqiu; ZHANG Sai; LIU Fengming; GUAN Chengcai; LIU Guofeng; DUAN Jin; JIANG Hongyan

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Polarization Image Super-Resolution Reconstruction Network Using Contextual Dual Self-Correlation Attention

更新时间：2026-07-21

- Polarization Image Super-Resolution Reconstruction Network Using Contextual Dual Self-Correlation Attention
- JOURNAL OF XI’AN JIAOTONG UNIVERSITY (2026)
- 作者机构：
  
  1.长春理工大学电子信息工程学院，130022，长春
  2.长春市吉海测控技术有限责任公司，130012，长春
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391
- Received：02 April 2026，
  
  Revised：2026-07-21，
  
  Accepted：21 July 2026，
- 稿件说明：
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CHEN Guangqiu, ZHANG Sai, LIU Fengming, et al. Polarization Image Super-Resolution Reconstruction Network Using Contextual Dual Self-Correlation Attention[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2026.
DOI：

CHEN Guangqiu, ZHANG Sai, LIU Fengming, et al. Polarization Image Super-Resolution Reconstruction Network Using Contextual Dual Self-Correlation Attention[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2026. DOI：

摘要

针对彩色分焦平面偏振图像全分辨率重建中，存在的颜色失真、边缘退化和偏振参数不一致问题，提出一种基于上下文双自相关注意力的偏振图像超分辨率重建网络。该网络由双路特征提取模块、上下文双自相关注意力Transformer和偏振特征重建模块组成。其中，双路特征提取模块用于增强浅层结构与纹理特征；上下文双自相关注意力Transformer通过上下文门控融合、位置感知自相关注意力机制和局部深度自相关注意力机制，建立不同偏振方向之间的空间结构关系与通道相关性；偏振特征重建模块通过特征精炼和像素重排恢复全分辨率彩色偏振图像。研究结果表明：在SunPol-Real、OL和Monno数据集上，所提网络对低分辨图像进行超分辨重建，0°、45°、90°和135°这4个方向图像的峰值信噪比的平均值分别达到38.16、39.39和40.02 dB，结构相似性的平均值分别达到0.967 6、0.965 8和0.981 5，从而验证了该网络能够改善边缘模糊、纹理缺失和偏振信息不一致的问题。该研究为彩色分焦平面偏振图像的超分辨率重建提供了一种有效的技术路径。

Abstract

To address the problems of color distortion

edge degradation

and inconsistency in polarization parameters during the full-resolution reconstruction of color division-of-focal-plane (DoFP) polarization images

a polarization image super-resolution reconstruction network based on contextual dual autocorrelation attention is proposed. The network consists of a dual-branch feature extraction module

a contextual dual-autocorrelation attention Transformer

and a polarization feature reconstruction module. Specifically

the dual-branch feature extraction module is employed to enhance shallow structural and texture features. The contextual dual-autocorrelation attention Transformer establishes spatial structural relationships and channel correlations among different polarization orientations through contextual gated fusion

position-aware autocorrelation attention

and local depth-wise autocorrelation attention mechanisms. The polarization feature reconstruction module restores full-resolution color polarization images through feature refinement and pixel rearrangement. Experimental results show that

on the SunPol-Real

and Monno datasets

the proposed network achieves average peak signal-to-noise ratio values of 38.16

39.39

and 40.02 dB

respectively

and average structural similarity index values of 0.967 6

0.965 8

and 0.981 5

respectively

across the reconstructed images at the 0°

45°

90°

and 135° polarization orientations. These results demonstrate that the proposed network can effectively alleviate edge blurring and texture loss while improving the consistency of polarization information. This study provides an effective technical approach for the super-resolution reconstruction of color DoFP polarization images.

关键词

Keywords

references

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