Swarm intelligence based optimisation in thermal image fusion using dual tree discrete wavelet transform

Abstract

In this paper, we propose a particle swarm optimised image fusion framework in Discrete Wavelet Transform domain (DWT) that combines the thermal image with the visual image to obtain a single informative fused image. Dual tree Discrete Wavelet Transform (DT-DWT) is applied for feature selection and Particle Swarm Optimisation technique is used to obtain the optimised image. In the fusion process, an optimised weighting factor has been used to construct a new composite image with maximum entropy and minimum Root Mean Square Error. We investigate the fusion performance of the proposed fusion algorithm with distorted input images by adding Gaussian white noise and blurring. The fusion results are compared with existing multi-resolution based fusion techniques such as Laplacian Pyramid, Gradient Pyramid, Ratio of Laplacian Pyramid and Shift Invariant Discrete Wavelet Transform. The quality of the fused image is assessed using seven quality metrics and the results indicate that our proposed method outperforms the state-of-the-art methods in both subjective and objective quality.

Keywords:

• Infrared thermography
• image fusion
• dual tree discrete wavelet transform
• particle swarm optimisation
• entropy
• root mean square error