Multifocus image fusion using artificial neural networks
Pattern Recognition Letters
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
Interactive digital photomontage
ACM SIGGRAPH 2004 Papers
Random Walks for 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
A novel similarity based quality metric for image fusion
Information Fusion
Multifocus image fusion using region segmentation and spatial frequency
Image and Vision Computing
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Foundations and Trends® in Computer Graphics and Vision
Multifocus image fusion using the nonsubsampled contourlet transform
Signal Processing
Image fusion based on a new contourlet packet
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Non-parametric and region-based image fusion with Bootstrap sampling
Information Fusion
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MRI and PET image fusion by combining IHS and retina-inspired models
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Multi-focus image fusion using PCNN
Pattern Recognition
Automatic segmentation of focused objects from images with low depth of field
Pattern Recognition Letters
Biological image fusion using a NSCT based variable-weight method
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Gradient-based multiresolution image fusion
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
Unsupervized Video Segmentation With Low Depth of Field
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we address the problem of fusing multi-focus images in dynamic scenes. The proposed approach consists of three main steps: first, the focus information of each source image obtained by morphological filtering is used to get the rough segmentation result which is one of the inputs of image matting. Then, image matting technique is applied to obtain the accurate focused region of each source image. Finally, the focused regions are combined together to construct the fused image. Through image matting, the proposed fusion algorithm combines the focus information and the correlations between nearby pixels together, and therefore tends to obtain more accurate fusion result. Experimental results demonstrate the superiority of the proposed method over traditional multi-focus image fusion methods, especially for those images in dynamic scenes.