Combining Regional Energy and Intuitionistic Fuzzy Sets for Infrared and Visible Image Fusion

Xing, Xiaoxue; Luo, Cong; Zhou, Jian; Yan, Minghan; Liu, Cheng; Xu, Tingfa

doi:10.3390/s21237813

Cited by 3 publications

(3 citation statements)

References 42 publications

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“…The proposed algorithm is compared with IGFF [12], AP-SPCNN [13], FPDE [14], RE-IFS [15], TT-CSR [16], YUVWT [17], IHSDCT [18], and the experimental results are objectively evaluated by the adaptive partition quality evaluation method of night vision anti-halation fusion image [27].…”

Section: Resultsmentioning

confidence: 99%

“…In reference [15], the designed fusion rules based on regional energy (RE) and intuitionistic fuzzy sets (IFS) preserve the important target and texture information in the resulting image, respectively. Reference [16] adopts tetrolet transform (TT) to decompose the visible and infrared image and use convolutional sparse representation (CSR) to fuse the high-frequency components, which effectively improves the visual effect of the fused image.…”

Section: Introductionmentioning

confidence: 99%

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Night Vision Anti-Halation Algorithm of Different-Source Image Fusion Based on Low-Frequency Sequence Generation

2023

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The abuse of high beam lights dazzles the opposite drivers when the vehicles meet at night, which can easily cause traffic accidents. The existing night vision anti-halation algorithms based on different-source image fusion can eliminate halation and obtain fusion images with rich color and details. However, the algorithms mistakenly eliminate some high-brightness important information. In order to address the problem, a night vision anti-halation algorithm based on low-frequency sequence generation is proposed. The low-frequency sequence generation model is constructed to generate image sequences with different degrees of halation elimination. According to the estimated illuminance for image sequences, the proposed sequence synthesis based on visual information maximization assigns a large weight to the areas with good brightness so as to obtain the fusion image without halation and with rich details. In four typical halation scenes covering most cases of night driving, the proposed algorithm effectively eliminates halation while retaining useful high-brightness information and has better universality than the other seven advanced comparison algorithms. The experimental results show that the fusion image obtained by the proposed algorithm is more suitable for human visual perception and helps to improve night driving safety.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Night Vision Anti-Halation Algorithm of Different-Source Image Fusion Based on Low-Frequency Sequence Generation

2023

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“…JunChen [2] introduced a MST decomposition model with enriched target for IR and VIS image fusion that enhances the target and preserves the texture features from both the input images and Laplacian pyramid is used to separate low and high frequency subbands. Xing [3] suggested IR and VIS image fusion conjoining regional energy and intuitionistic fuzzy sets (IFS). NSST transform is used for decomposition in to sub-bands.…”

mentioning

confidence: 99%

Infrared and visible image fusion using LatLRR and ResNet

Prema,

Arivazhagan,

Aishwarya

et al. 2024

AIP Conference Proceedings

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Image fusion is a notable concern in the image processing field which helps to produce a resultant image by amalgamating all the features from one or more source images. We envisioned an infrared (IR) and visible (VIS) image fusion technique combining (Latent Low Rank Representation) LatLRR and ResNet to mitigate the loss of contrast, texture, spectral, and spatial details in the resultant image. The fused image seems to be more appropriate and obvious for human and machine vision perception. Using LatLRR, the source images are initially broken down into saliency and latent low rank parts. Then, using weighted-average method, the Latent Low Rank components are integrated. Fusion approach based on deep features is used to overcome the problem of information lost by using RESNET to extract multilayer features for the saliency part as this network has a skip connection which helps to gather information from every layer. After that, the deep features are normalized by nuclear norm, and preliminary weight maps are generated. Combining the original weight maps with a Soft-Max operation yields the final-weight maps. At last, by summing the fused Latent Low Rank component and the Saliency content, the fused image will be reconstructed. Both visual and quantitative performance evaluation is done for the proposed method with five existing methods and it is inferred from the results that the proposed method can well reconstruct the fused image without haloes and artifacts.

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Infrared and visible military image fusion strategies and applications based on composite decomposition and multi-fuzzy theory

Wang,

Du,

Lin

et al. 2024

Preprint

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It is found in infrared military targets’detection that some of the collected images are greatly affected by the environment, and they are still not provide targets’detailed information after preprocessing, which limits the detection effect. In this paper, we establish a military infrared-visible dataset and propose a military image fusion strategy based on composite decomposition and multi-fuzzy theory. Firstly, the source infrared and visible images are decomposed by using the two-scale and Latent Low-rank representation composite method, and the underlying optimal information of the images is mined. Secondly, for low-frequency detail images, the Gaussian fuzzy function is used to adjust the visual saliency map weighting function; for low-frequency salient images, the Cauchy fuzzy function is used to adjust the image energy weighting; and for high-frequency images, an improved intuitionistic fuzzy set function is used as the fusion rule as proposed. Finally, four sets of typical images are used to test the proposed method and make both subjective and objective comparisons with the other four mainstream fusion methods. The application experiment results show that the proposed military image fusion strategy can more effectively improve the clarity of the data source and thus improve military targets’detection accuracy.

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Combining Regional Energy and Intuitionistic Fuzzy Sets for Infrared and Visible Image Fusion

Cited by 3 publications

References 42 publications

Night Vision Anti-Halation Algorithm of Different-Source Image Fusion Based on Low-Frequency Sequence Generation

Night Vision Anti-Halation Algorithm of Different-Source Image Fusion Based on Low-Frequency Sequence Generation

Infrared and visible image fusion using LatLRR and ResNet

Infrared and visible military image fusion strategies and applications based on composite decomposition and multi-fuzzy theory

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