Image fusion of dual-discriminator generative adversarial network and latent low-rank representation

YUAN Daiyu; YUAN Lihua; XI Tengyan; LI Zhe

doi:10.37188/OPE.20233107.1085

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Image fusion of dual-discriminator generative adversarial network and latent low-rank representation

Information Sciences | 更新时间：2026-09-12

- Image fusion of dual-discriminator generative adversarial network and latent low-rank representation
- Optics and Precision Engineering Vol. 31, Issue 7, Pages: 1085-1095(2023)
- 作者机构：
  
  1.南昌航空大学无损检测教育部重点实验室，江西南昌 330063
  2.中国航发沈阳黎明航空发动机有限责任公司，辽宁沈阳 110043
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233107.1085
  CLC： TN219
- Received：09 August 2022，
  
  Revised：2022-09-26，
  
  Published：10 April 2023
- 稿件说明：
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袁代玉,袁丽华,习腾彦等.潜在低秩表示下的双判别器生成对抗网络的图像融合[J].光学精密工程,2023,31(07):1085-1095.

YUAN Daiyu,YUAN Lihua,XI Tengyan,et al.Image fusion of dual-discriminator generative adversarial network and latent low-rank representation[J].Optics and Precision Engineering,2023,31(07):1085-1095.
袁代玉,袁丽华,习腾彦等.潜在低秩表示下的双判别器生成对抗网络的图像融合[J].光学精密工程,2023,31(07):1085-1095. DOI： 10.37188/OPE.20233107.1085.

YUAN Daiyu,YUAN Lihua,XI Tengyan,et al.Image fusion of dual-discriminator generative adversarial network and latent low-rank representation[J].Optics and Precision Engineering,2023,31(07):1085-1095. DOI： 10.37188/OPE.20233107.1085.

摘要

为了改善红外与可见光图像融合的视觉效果，通过潜在低秩表示将两种不同源的图像分别分解为各自的低秩分量和去除噪声的稀疏分量，采用KL变换确定权重对稀疏分量进行加权融合得到融合稀疏图。再对双判别器的生成对抗网络重设计，借助VGG16网络提取两种源的低秩分量特征作为该网络的输入，通过生成器和判别器的博弈来生成融合低秩图。最后，将融合稀疏图与融合低秩图进行叠加获得最终的融合结果。实验结果表明，在TNO数据集上，与所列的5种先进方法相比，本文所提出的方法在熵、标准差、互信息、差异相关性总和及多尺度结构相似度5种指标上均获得最优结果，相比于次优值，5种指标分别提高了2.43%，4.68%，2.29%，2.24%，1.74%。在RoadScene数据集上只在差异相关性总和及多尺度结构相似度两种指标上取得最优，另外3种指标仅次于GTF（gradient transfer and total variation minimization）方法，但图像视觉效果明显优于GTF方法。综合主观评价和客观评价分析，本文所提方法确实能获得高质量的融合图像，与多种方法相比具有明显的优势。

Abstract

To improve the visual effect of infrared and visible image fusion， images from two different sources were decomposed into low-rank images and sparse images with noise removed by latent low-rank representation. Moreover， to obtain the fusion sparse plot， the KL transformation was used to determine the weights and weighted fusion of the sparse components. The generation adversarial network of the double discriminator was redesigned， and the low-rank component characteristics of the two sources were extracted as the inputs of the network through the VGG16 network. The fusion low-rank diagram was generated using the game of generator and discriminator. Finally， the fusion sparse image and the fusion low-rank image were superimposed to obtain the final fusion result. Experimental results showed that on the TNO dataset， compared with the five listed advanced methods， the five indicators of entropy， standard deviation， mutual information， sum of difference correlation， and multi-scale structural similarity increased by 2.43%， 4.68%， 2.29%， 2.24%， and 1.74%， respectively， when using the proposed method. For the RoadScene dataset， only two metrics， namely， the sum of the difference correlation and multi-scale structural similarity， were optimal. The other three metrics were second only to the GTF method. However， the image visualization effect was significantly better than the GTF method. Based on subjective evaluation and objective evaluation analysis， the proposed method can obtain high-quality fusion images， which has obvious advantages compared with the comparison method.

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Keywords

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Related Institution

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