Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Guest editorial: Image fusion: Advances in the state of the art
Information Fusion
Pixel- and region-based image fusion with complex wavelets
Information Fusion
Remote sensing image fusion using the curvelet transform
Information Fusion
Multifocus image fusion using region segmentation and spatial frequency
Image and Vision Computing
Multifocus image fusion by combining curvelet and wavelet transform
Pattern Recognition Letters
Efficient fusion scheme for multi-focus images by using blurring measure
Digital Signal Processing
Multifocus image fusion using the nonsubsampled contourlet transform
Signal Processing
Pixel-level image fusion with simultaneous orthogonal matching pursuit
Information Fusion
A regional image fusion based on similarity characteristics
Signal Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Coherent Multiscale Image Processing Using Dual-Tree Quaternion Wavelets
IEEE Transactions on Image Processing
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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.