An effective fuzzy logic and particle swarm optimization based thermal and visible-light image fusion framework using curve-let transform

Abstract

Thermal and visible-light image fusion is an active and one of the most significant research area in image processing. The result produced through this fusion process can be very much useful in surveillance and military applications especially for improving visibility and situation awareness. This paper proposes an effective fuzzy logic and particle swarm optimization based thermal and visible-light image fusion framework using curve-let transform. A novel fuzzy logic based contrast enhancement method is introduced in this framework for improving the visual appearance of both thermal and visible-light source images. In this framework curve-let transform is used for extracting more multi-scale features from source images. Upon applying curve-let transform on enhanced source images will produce low frequency (approximation) and high frequency (detailed) coefficients. Particle swarm optimization based fusion strategy has been used for producing more featured fused approximation coefficients. Correspondingly Max fusion strategy has been used for generating more textures based fused detailed coefficients. Finally upon applying inverse curve-let transform generates final fused image. The obtained simulation results clearly demonstrate that our framework has shown superior results compared to other fusion methods both in terms of subjective and objective assessments.