Multisensor image fusion using fast discrete curvelet transform

This paper describes a novel approach to multisensor image fusion using a new mathematical transform: the curvelet transform. The transform has shown promising results over wavelet transform for 2-D signals. Wavelets, though well suited to point singularities have limitation with orientation selectivity, and therefore, do not represent two-dimensional singularities (e.g. smooth curves) effectively. Curvelet improves wavelet by incorporating a directional component. This paper employs the curvelet transform for image fusion. Based on the local energy of direction curvelet subbands, we give the definition of local band-limited contrast and use it as one of the fusion rules. The local band-limited contrast can reflect the response of local image features in human visual system truly. When used to image fusion in noiseless circumstance, it is effective. But in noisy circumstance, it is not always robust. According to the different characteristics between image features and noise, the local directional energy entropy is proposed. It can distinguish the noise and local image features. In this paper, the combination of local band-limited contrast and local directional energy entropy is used as image fusion. Experimental results show that it is robust in noisy and noiseless image fusion system.