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Article

Multi-Focus Image Fusion Based on Decision Map and Sparse Representation

School of Electrical & Electronic Engineering, North China Electric Power University, Beijing 100226, China
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Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(17), 3612; https://doi.org/10.3390/app9173612
Received: 24 July 2019 / Revised: 23 August 2019 / Accepted: 25 August 2019 / Published: 2 September 2019
(This article belongs to the Special Issue Advanced Ultrafast Imaging)
As the focal length of an optical lens in a conventional camera is limited, it is usually arduous to obtain an image in which each object is focused. This problem can be solved by multi-focus image fusion. In this paper, we propose an entirely new multi-focus image fusion method based on decision map and sparse representation (DMSR). First, we obtained a decision map by analyzing low-scale images with sparse representation, measuring the effective clarity level, and using spatial frequency methods to process uncertain areas. Subsequently, the transitional area around the focus boundary was determined by the decision map, and we implemented the transitional area fusion based on sparse representation. The experimental results show that the proposed method is superior to the other five fusion methods, both in terms of visual effect and quantitative evaluation. View Full-Text
Keywords: multi-focus image fusion; low-scale images; decision map; sparse representation; transitional area multi-focus image fusion; low-scale images; decision map; sparse representation; transitional area
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MDPI and ACS Style

Liao, B.; Chen, H.; Mo, W. Multi-Focus Image Fusion Based on Decision Map and Sparse Representation. Appl. Sci. 2019, 9, 3612. https://doi.org/10.3390/app9173612

AMA Style

Liao B, Chen H, Mo W. Multi-Focus Image Fusion Based on Decision Map and Sparse Representation. Applied Sciences. 2019; 9(17):3612. https://doi.org/10.3390/app9173612

Chicago/Turabian Style

Liao, Bin, Hua Chen, and Wei Mo. 2019. "Multi-Focus Image Fusion Based on Decision Map and Sparse Representation" Applied Sciences 9, no. 17: 3612. https://doi.org/10.3390/app9173612

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