Multifocus image fusion using region segmentation and spatial frequency

By Shutao Li, Bin Yang. Image and Vision Computing, 2008

460 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Multifocus Image Fusion and Restoration With Sparse Representation (2010, methodology): “The first category is the spatial domain-based methods, which directly fuse the source images into the intensity values [10]–[13].”
A non-reference image fusion metric based on mutual information of image features (2011, methodology): “Visual sensor networks [2], remote sensing [3,4], digital camera vision [5,6], medical imaging [7–9], defect inspection [10,11], over-exposure correction [12], military and surveillance applications [13–15], are some practical examples.”
Multi-focus image fusion with a deep convolutional neural network (2017, methodology): “In our method, as with most spatial domain multi-focus image fusion methods [18–22, 24– 27, The obtained binary map is shown in Fig.”
Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation (2020, unknown): “A region-based method, which incorporated segmentation into the fusion process, was proposed by S. Li in [219] .”
Pixel-level image fusion: A survey of the state of the art (2017, methodology): “AC CE PT ED M AN US CR IP T image fusion methods perform fusion to the regions with irregular shapes which are obtained by image segmentation [64, 65].”