Abstract
Image fusion technology combines information from different source images of the same target and performs extremely effective information complementation, which is widely used for the transportation field, medicine field, and surveillance field. Specifically, due to the limitation of depth of field in imaging device, images cannot focus on all objects and miss partial details. To deal with this problem, an effective multi-focus image fusion method is proposed in this paper. We interpret the production of the focus map as a two-class classification task and solve this problem by using a method based on the cascade-forest model. Firstly, we extract the specific features from overlapping patches to represent the clarity level of source images. To obtain the focus map, feature vectors are fed into the pre-trained cascade-forest model. Then, we utilize consistency check to acquire the initial decision map. Afterward, guided image filtering is used for edge-reservation to refine the decision map. Finally, the result is obtained through pixel-wise weighted average strategy. Extensive experiments demonstrate that the proposed method achieves outstanding visual performance and excellent objective indicators.
Highlights
1 Introduction Image fusion technology combines information from different source images of the same target, which is more conducive to comprehensive access to the target and the scene information
Numerous multi-focus image fusion methods were proposed during the past few decades, which can be roughly divided into transform domain-based methods and spatial domain-based methods [5]
This paper proposes a novel multi-focus image fusion method based on the cascade-forest model
Summary
Image fusion technology combines information from different source images of the same target, which is more conducive to comprehensive access to the target and the scene information. This paper proposes a novel multi-focus image fusion method based on the cascade-forest model. Spatial domain-based methods often suffer from the limitation of fusion rule To address this problem, we interpret the production of the focus map as a two-class classification task and utilize the cascaded-forest model as an effective fusion rule. We interpret the production of the focus map as a two-class classification task and utilize the cascaded-forest model as an effective fusion rule. For most spatial domain-based methods, they often produce undesirable artifacts around the boundaries between the focused and non-focused regions To address this problem, we employ the guided image filter to refine the initial decision map for better edgereservation.
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