An Image Fusion Method Based on Fuzzy Regional Characteristics

Yong, Jun; Zhang, Sheng Wei; Yue, Cai

doi:10.4028/www.scientific.net/amm.457-458.1097

Cited by 1 publication

(3 citation statements)

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“…According to the theoretical basis mentioned in Section 2.1, the cascade encoder is used to extract redundant and complementary features from infrared and visible images, and transforms these features into different spaces separately. The extracted redundant features and complementary features are fused with different fusion strategies softly directed by the fuzzy regional characteristics (FRC) method [20,21]. The FRCbased feature-weighted fusion strategy is used to fuse the redundancy feature, while the FRC-based feature choose-max fusion strategy is adopted to integrate the complementary feature.…”

Section: The Proposed Image Fusion Frameworkmentioning

confidence: 99%

“…Significantly, the region containing the target information in the redundant information can be effectively fused. Hence, it is suitable for complementary features and redundant features in the hidden layer to use the fusion rule that is designed based on the idea of the FRC method [20,21].…”

Section: Frc-based Fusion Strategymentioning

confidence: 99%

“…The joint cascade encoder is used to extract redundant and complementary feature maps from source images, enabling the feature maps to contain more source images information. (2) In the fusion layer, multiscale salient feature maps are merged by manually designed fusion strategies based on fuzzy region characteristics (FRC) [20,21]. The fusion strategy under the fuzzy region rule is adopted to protect regional consistency of the fused image.…”

Section: Introductionmentioning

confidence: 99%

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JCa2Co: A joint cascade convolution coding network based on fuzzy regional characteristics for infrared and visible image fusion

Liu

Xiao

et al. 2021

IET comput. vis

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To facilitate the extraction of source image information, and preserve the consistency of heterogeneous regional features, a multiscale image fusion method based on a joint cascaded convolutional coding network (JCa2Co) is proposed. The JCa2Co network can retain vast quantities of information from source images in a multiscale perspective. The approach includes an encoder, a fusion layer and decoder. In the fusion layer, the Fuzzy Regional Characteristics (FRC) scheme is considered, and the multiscale feature maps are extracted from image subregions to ensure regional image consistency. Firstly, a joint cascaded encoder is used to extract multiscale features of the source image, in which the output of each layer is connected to every other layer. The fusion layer based on FRC is then performed to fuse each scale feature. Finally, the fused image is reconstructed by the decoder. In addition, to verify the regional consistency of the fused image, a regional consistency measure is proposed. Experiments are performed on the TNO Image Fusion Database. The experimental results exhibit that the proposed JCa2Co method has better comprehensive performance than the eight state-of-the-art fusion methods. Moreover, it can effectively integrate meaningful information in infrared and visible images and has excellent performance in objective evaluation and visual quality, which is beneficial to target recognition and tracking.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: The Proposed Image Fusion Frameworkmentioning

confidence: 99%

Section: Frc-based Fusion Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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