Abstract
In this paper, a multi-focus image fusion method based on Dual-Tree Compactly Supported Shearlet Transform (DT CSST) and Direction Decision Map (DDM) is proposed. DT CSST is a shift invariant modification of conventional Compactly Supported Shearlet Transform (CSST). Based on the mitigation of shift variance of CSST in DT CSST, a clearer fused image could be acquired through the General Image Fusion (GIF) method, and this image is called the initial fused image in this paper. The decision map is determined by the similarity of the initial fused image and the source images. The generation algorithm of the decision map in this paper takes advantage of the directional nature of DT CSST: every direction of the transform generates an initial directional decision map and then yields the final map through vote and smooth steps. This scheme is called DDM in this paper. The proposed method is evaluated by four groups of standard images. The results show that the proposed method is able to improve the quality indices compared with two algorithms which have excellent quality indices.
Highlights
Image fusion is a long-studied field that is attracting everincreasing attention for a number of applications, such as remote sensing, navigation for robots, etc
Many Multi-Scale Transform (MST) are introduced into these methods, such as Discrete Wavelet Transform (DWT) [1,2,3,4,5], Curvelet [6, 7], Band Limited Shearlet [8] and Compactly Supported Shearlet Transform (CSST) [9, 10], etc
The similarity is recorded by a decision map, and the final fused image is the weighted sum of the clearer areas of the source images and the initial fused image
Summary
Image fusion is a long-studied field that is attracting everincreasing attention for a number of applications, such as remote sensing, navigation for robots, etc. The similarity is recorded by a decision map, and the final fused image is the weighted sum of the clearer areas of the source images and the initial fused image This method is motivated by the fusion of noise images, while the scheme can improve the quality of clear images. In another paper [13], Li et al gave more details about how to calculate the decision map from both regional RMSE and Correlation Coefficient (CC), and a dual-window technique is performed to calculate the final fused images. Both methods are successful because the fused images have good perceptions and the quality indices are higher than many methods. The decision map is based on RMSE, and it has been verified that RMSE cannot reflect the quality of
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