Infrared and visible image fusion can obtain an integrated image containing obvious object information and high spatial resolution background information. Therefore, combining the characteristics of infrared and visible images to obtain the fused image has important research significance. In this paper, an effective fusion algorithm based on non-subsampled contourlet transform (NSCT) is proposed. The method is based on the application of a modulated pulse-coupled neural network fusion (PCNN) strategy and an energy attribute fusion strategy in the NSCT domain. First, NSCT is used to decompose the input original image into low frequency sub-images and high frequency sub-images. Then, the high frequency sub-images are fused via a multi-level morphological gradient (MLMG) domain PCNN and the low frequency sub-images are fused via the energy attribute fusion strategy. Finally, the fused sub-images are reconstructed by inverse NSCT. Experimental results demonstrate that the proposed algorithm has a better fusion performance in both subjective evaluation and objective evaluation.
Aiming at the problem of the traditional neural network for non-uniformity correction easy to cause ghosting artifacts and image blurring, an improved non-uniformity correction algorithm based on neural network is proposed. Firstly, a new fast trilateral filter is designed, which can be regarded as an edge-preserving smoothing operator. Secondly, in order to stabilize and accelerate the learning process, it adopts the self-adaptive learning rate and applies additional momentum factor to the neural network. Thirdly, in order to update the calibration parameters accurately, the local motion of different areas is judged carefully. The simulating experiments indicate that the proposed algorithm can suppress the ghosting artifacts and the image degradation. And it has better performance compared with other algorithms.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.