Region level based multi-focus image fusion using quaternion wavelet and normalized cut

Region level based methods are popular in recent years for multifocus image fusion as they are the most direct fusion ways. However, the fusion result is not ideal due to the difficulty in focus region segmentation. In this paper, we propose a novel region level based multifocus image fusion method that can locate the boundary of the focus region accurately. As a novel tool of image analysis, phases in the quaternion wavelet transform (QWT) are capable of representing the texture information in the image. We use the local variance of the phases to detect the focus or defocus for every pixel initially. Then, we segment the focus detection result by the normalized cut to remove detection errors, thus initial fusion result is acquired through copying from source images according to the focus detection results. Next, we compare initial fusion result with spatial frequency weighted fusion result to accurately locate the boundary of the focus region by structural similarity. Finally, the fusion result is obtained using spatial frequency as fusion weight along the boundary of the focus region. Furthermore, we conduct several experiments to verify the feasibility of the fusion framework. The proposed algorithm is demonstrated superior to the reference methods. •We propose a novel region-level fusion method using quaternion wavelet transform and normalized cut.•We exploit phase information of quaternion wavelet to detect the focus region roughly.•The normalized cut based segmentation method helps to refine the result of the focus region detection.•We exploit the spatial frequency and structural similarity index to improve the visual quality of the fusion result.