Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Multisensor image fusion using the wavelet transform
Graphical Models and Image Processing
Image Sequence Fusion Using a Shift-Invariant Wavelet Transform
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
Evaluation of focus measures in multi-focus image fusion
Pattern Recognition Letters
Guest editorial: Image fusion: Advances in the state of the art
Information Fusion
Pixel- and region-based image fusion with complex wavelets
Information Fusion
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
Segmentation-driven image fusion based on alpha-stable modeling of wavelet coefficients
IEEE Transactions on Multimedia
Fusion of multi-focus images using differential evolution algorithm
Expert Systems with Applications: An International Journal
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Multi-focus image fusion aims to combine a set of images that are captured from the same scene but with different focuses for producing another sharper image. The critical issue in the design of multi-focus image fusion algorithms is to evaluate the local content information of the input images. Motivated by the observation that the marginal distribution of the wavelet coefficients is different for images with different focus levels, a new statistical sharpness measure is proposed in this paper by exploiting the spreading of the wavelet coefficients distribution to measure the degree of the image's blur. Furthermore, the wavelet coefficients distribution is evaluated using a locally adaptive Laplacian mixture model. The proposed sharpness measure is then exploited to perform adaptive image fusion in wavelet domain. Extensive experiments are conducted using three sets of test images under three objective metrics to demonstrate the superior performance of the proposed approach.