A Fractal Dimension Based Framework for Night Vision Fusion

In this paper, a novel fusion framework is proposed for night-vision applications such as pedestrian recognition, vehicle navigation and surveillance. The underlying concept is to combine low-light visible and infrared imagery into a single output to enhance visual perception. The proposed framework is computationally simple since it is only realized in the spatial domain. The core idea is to obtain an initial fused image by averaging all the source images. The initial fused image is then enhanced by selecting the most salient features guided from the root mean square error (RMSE) and fractal dimension of the visual and infrared images to obtain the final fused image. Extensive experiments on different scene imaginary demonstrate that it is consistently superior to the conventional image fusion methods in terms of visual and quantitative evaluations.