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Article

Multi-Focus Image Fusion Method Based on Multi-Scale Decomposition of Information Complementary

1
College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2
College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China
3
College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
*
Author to whom correspondence should be addressed.
Academic Editors: Jiayi Ma, Yu Liu, Junjun Jiang, Zheng Wang and Han Xu
Entropy 2021, 23(10), 1362; https://doi.org/10.3390/e23101362
Received: 10 August 2021 / Revised: 12 October 2021 / Accepted: 14 October 2021 / Published: 19 October 2021
(This article belongs to the Special Issue Advances in Image Fusion)
Multi-focus image fusion is an important method used to combine the focused parts from source multi-focus images into a single full-focus image. Currently, to address the problem of multi-focus image fusion, the key is on how to accurately detect the focus regions, especially when the source images captured by cameras produce anisotropic blur and unregistration. This paper proposes a new multi-focus image fusion method based on the multi-scale decomposition of complementary information. Firstly, this method uses two groups of large-scale and small-scale decomposition schemes that are structurally complementary, to perform two-scale double-layer singular value decomposition of the image separately and obtain low-frequency and high-frequency components. Then, the low-frequency components are fused by a rule that integrates image local energy with edge energy. The high-frequency components are fused by the parameter-adaptive pulse-coupled neural network model (PA-PCNN), and according to the feature information contained in each decomposition layer of the high-frequency components, different detailed features are selected as the external stimulus input of the PA-PCNN. Finally, according to the two-scale decomposition of the source image that is structure complementary, and the fusion of high and low frequency components, two initial decision maps with complementary information are obtained. By refining the initial decision graph, the final fusion decision map is obtained to complete the image fusion. In addition, the proposed method is compared with 10 state-of-the-art approaches to verify its effectiveness. The experimental results show that the proposed method can more accurately distinguish the focused and non-focused areas in the case of image pre-registration and unregistration, and the subjective and objective evaluation indicators are slightly better than those of the existing methods. View Full-Text
Keywords: multi-focus image fusion; singular value decomposition; multi-scale decomposition; PA-PCNN; quaternion; joint bilateral filter multi-focus image fusion; singular value decomposition; multi-scale decomposition; PA-PCNN; quaternion; joint bilateral filter
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MDPI and ACS Style

Wan, H.; Tang, X.; Zhu, Z.; Li, W. Multi-Focus Image Fusion Method Based on Multi-Scale Decomposition of Information Complementary. Entropy 2021, 23, 1362. https://doi.org/10.3390/e23101362

AMA Style

Wan H, Tang X, Zhu Z, Li W. Multi-Focus Image Fusion Method Based on Multi-Scale Decomposition of Information Complementary. Entropy. 2021; 23(10):1362. https://doi.org/10.3390/e23101362

Chicago/Turabian Style

Wan, Hui, Xianlun Tang, Zhiqin Zhu, and Weisheng Li. 2021. "Multi-Focus Image Fusion Method Based on Multi-Scale Decomposition of Information Complementary" Entropy 23, no. 10: 1362. https://doi.org/10.3390/e23101362

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