A fast and effective image fusion method is proposed for creating a highly informative fused image through merging multiple images. The proposed method is based on a two-scale decomposition of an image into a base layer containing large scale variations in intensity, and a detail layer capturing small scale details. A novel guided filtering-based weighted average technique is proposed to make full use of spatial consistency for fusion of the base and detail layers. Experimental results demonstrate that the proposed method can obtain state-of-the-art performance for fusion of multispectral, multifocus, multimodal, and multiexposure images.

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Image Fusion With Guided Filtering

Pixel-level image fusion

Infrared and visible image fusion methods and applications: A survey

A general framework for multiresolution image fusion: from pixels to regions

Infrared and visible image fusion via gradient transfer and total variation minimization

Mutual information is proposed as an information measure for evaluating image fusion performance. The proposed measure represents how much information is obtained from the input images. No assumption is made regarding the nature of the relation between the intensities in both input modalities. The results show that the measure is meaningful and explicit.

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Information measure for performance of image fusion

An approach to the problem of multimodality medical image fusion is proposed, using two-dimensional discrete wavelet transform(2-D DWT). Local image fusion mechanism employing 2-D periodic DWT is also presented, which decreases the calculation time and space cost. To evaluate the fusion result, mutual information (MI) is employed as a performance measurement. The experiment results demonstrate that the fusion scheme of this study is very well suited for clinical applications, especially for treatment plan when complementary information is needed.

Medical image fusion using two-dimensional discrete wavelet transform

A new method for reconstruction of surface from cross-sectional contours is presented based on iterated function system (IFS). The properties of the algorithm are, nonlinear and stochastic. The experiment result shows that the proposed scheme can accurately preserves the shape of the contours with a very fast calculation speed.

Guihong Qu

Papers

Information measure for performance of image fusion

Medical image fusion using two-dimensional discrete wavelet transform

IFS-based approach to surface reconstruction from cross-sectional contours