An Image Fusion Algorithm Based on Ant Lion Optimized Maximum Entropy Segmentation and Guided Filtering

JIANG Jiewei; LIU Shanghui; JIN Ku; WEI Xumeng; GONG Jiamin

doi:10.11999/JEIT221499

Volume 45 Issue 4

Apr. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(4): 1391-1400

JIANG Jiewei, LIU Shanghui, JIN Ku, WEI Xumeng, GONG Jiamin. An Image Fusion Algorithm Based on Ant Lion Optimized Maximum Entropy Segmentation and Guided Filtering[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1391-1400. doi: 10.11999/JEIT221499

Citation:

JIANG Jiewei, LIU Shanghui, JIN Ku, WEI Xumeng, GONG Jiamin. An Image Fusion Algorithm Based on Ant Lion Optimized Maximum Entropy Segmentation and Guided Filtering[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1391-1400. doi: 10.11999/JEIT221499

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An Image Fusion Algorithm Based on Ant Lion Optimized Maximum Entropy Segmentation and Guided Filtering

doi: 10.11999/JEIT221499

1.
School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2.
School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China

Funds: The National Natural Science Foundation of China (61775180, 62276210), The Natural Science Basic Research Program of Shaanxi (2022JM-380)

Received Date: 2022-12-02
Rev Recd Date: 2022-12-23

Available Online: 2022-12-28

Publish Date: 2023-04-10

Abstract

Abstract

Traditional fusion algorithms of infrared and visible images often have defects such as insufficient target extraction and loss of details, which lead to unsatisfactory fusion effects, and the fused image can not be applied to target detection, tracking or recognition. Therefore, a fusion method of infrared and visible images based on guided filtering and improved maximum Shannon entropy segmentation method using Ant Lion Optimization algorithm (ALO) is proposed. First, Ant Lion Optimized Maximum Entropy Segmentation (ALO-MES) algorithm is used to extract the target from infrared image. Then, the Non-Subsampled Shearlet Transform (NSST) is performed on the infrared and visible images to obtained the low frequency and high frequency sub-bands, and conduct guided filtering for obtained sub-bands. The low-frequency fusion coefficient is obtained from the extracted target image and the enhanced infrared and visible low-frequency components through the fusion rule based on ALO-MES. And the high-frequency fusion coefficient is obtained by the enhanced high-frequency sub-bands components through Dual-Channel Spiking Cortical Model (DCSCM). Finally, the fusion image is obtained by inverse NSST transform. The experimental results show that the proposed algorithm can get fusion image with clear target and background information.
- Image fusion,
- Ant Lion Optimization (ALO) algorithm,
- Maximum Shannon entropy segmentation,
- Guided filtering,
- Dual Channel Spiking Cortical Model (DCSCM)

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References(23)

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