An Adaptive Fusion Algorithm for Visible and Infrared Videos Based on Entropy and the Cumulative Distribution of Gray Levels

Visible videos captured under different weather conditions may exhibit different characteristics, and thermal infrared videos are easily affected by ambient temperature variations; this sensitivity to environmental conditions makes the fusion of visible and thermal infrared videos a challenge. This paper proposes an adaptive fusion algorithm for visible and infrared videos, and uses cumulative distribution of gray levels and the entropy to adaptively retain infrared-hot targets and visible textures. The original visible and infrared frames are decomposed into two layers, namely, the base layer and the detail layer. The guided filter is employed to decompose frames due to its high efficiency. Two weight maps, one for the infrared base layer and one for the visible base layer, are adaptively generated based on the cumulative distribution of gray levels and the entropy, respectively. The visible base layer and the infrared base layer are fused based on their weight maps. The final fusion result is obtained by combining the fused base layer with the visible detail layer. Experimental results demonstrate that the proposed algorithm can achieve better fusion results compared with state-of-the-art methods.