Abstract
Vision sensor systems (VSS) are widely deployed in surveillance, traffic and industrial contexts. A large number of images can be obtained via VSS. Because of the limitations of vision sensors, it is difficult to obtain an all-focused image. This causes difficulties in analyzing and understanding the image. In this paper, a novel multi-focus image fusion method (SRGF) is proposed. The proposed method uses sparse coding to classify the focused regions and defocused regions to obtain the focus feature maps. Then, a guided filter (GF) is used to calculate the score maps. An initial decision map can be obtained by comparing the score maps. After that, consistency verification is performed, and the initial decision map is further refined by the guided filter to obtain the final decision map. By performing experiments, our method can obtain satisfying fusion results. This demonstrates that the proposed method is competitive with the existing state-of-the-art fusion methods.
Highlights
IntroductionMansour et al [35] proposed a novel multi-focus image fusion method based on sparse representation (SR) with a guided filter, and the Markov random field was utilized to refine the decision map in their method
A large number of images can be obtained via vision sensor systems (VSS)
Mansour et al [35] proposed a novel multi-focus image fusion method based on sparse representation (SR) with a guided filter, and the Markov random field was utilized to refine the decision map in their method
Summary
Mansour et al [35] proposed a novel multi-focus image fusion method based on SR with a guided filter, and the Markov random field was utilized to refine the decision map in their method. Compared with these traditional SR-based methods, there are three major contributions: We use sparse coefficients to classify the focused regions and the unfocused regions to build an initial decision map, as shown, rather than directly fusing the sparse coefficients.
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