Multisensor image fusion using the wavelet transform
Graphical Models and Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multifocus image fusion using artificial neural networks
Pattern Recognition Letters
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
Multi-focus image fusion using pulse coupled neural network
Pattern Recognition Letters
Image and Vision Computing
Multi-focus image fusion using PCNN
Pattern Recognition
Fusion of multi-focus images using differential evolution algorithm
Expert Systems with Applications: An International Journal
A color multi-focus image fusion algorithm with nonsubsampled contourlet transform in space domain
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 3
On the focusing of thermal images
Pattern Recognition Letters
Pattern Recognition Letters
Multi-focus image fusion based on the neighbor distance
Pattern Recognition
Analysis of focus measure operators for shape-from-focus
Pattern Recognition
Multi-focus thermal image fusion
Pattern Recognition Letters
Mutual spectral residual approach for multifocus image fusion
Digital Signal Processing
Focusing in thermal imagery using morphological gradient operator
Pattern Recognition Letters
Spiking cortical model for multifocus image fusion
Neurocomputing
A comparison of contrast measurements in passive autofocus systems for low contrast images
Multimedia Tools and Applications
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Several focus measures were studied in this paper as the measures of image clarity, in the field of multi-focus image fusion. All these focus measures are defined in the spatial domain and can be implemented in real-time fusion systems with fast response and robustness. This paper proposed a method to assess focus measures according to focus measures' capability of distinguishing focused image blocks from defocused image blocks. Experiments were conducted on several sets of images and results show that sum-modified-Laplacian (SML) can provide better performance than other focus measures, when the execution time is not included in the evaluation.