A new automated quality assessment algorithm for image fusion

By Yin Chen, Rick S. Blum. Image and Vision Computing, 2009

491 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.

A non-reference image fusion metric based on mutual information of image features (2011, methodology): “On the other hand, it is reasonable that some fusion methods perform better for some certain databases, but do not work well for others when the images change [29].”
Multi-focus image fusion with a deep convolutional neural network (2017, unknown): “4) Human perception-based metric Q CB proposed by Chen and Blum [63] , which addresses the major features in human visual system.”
Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study (2012, methodology): “Some methods to compare the subjective and objective assessment were presented in previous studies [23], [31], [32].”
Perceptual Quality Assessment for Multi-Exposure Image Fusion (2015, background): “Over the past decade, substantial effort has been made to develop objective IQA models for image fusion applications [18]–[26].”
Simultaneous image fusion and denoising with adaptive sparse representation (2015, background): “The calculation of Q CB is relative complex and more details can be found in [41].”
Image Fusion With Convolutional Sparse Representation (2016, methodology): “Four popular objective metrics, namely, the entropy EN, the gradient-based metric QAB/F [35], the HVS-based metric QHVS [36] and the visual information fidelity fusion metric VIFF [37], are adopted to evaluate fusion performance.”