Image Fusion Based on Improved Maximum Entropy Segmentation Algorithm and Rolling Guidance Filter
With the change of application environment,the engineering deployment capability of numerous fusion algorithms for infrared and visible images is generally poor.These algorithms exhibit various shortcoming,including insufficient target extraction,loss of details,algorithmic complexity,low efficiency,and limited applicability.To address the aforementioned challenges,a fusion method for infrared and visible images is proposed by leveraging an improved maximum entropy algorithm and a rolling guided filter.First,infrared targets are extracted through using improved maximum entropy algorithm,and the visible image and infrared image are decomposed into basic layer and detail layer through using the scale-awareness and edge-preservation characteristics of rolling guided filter.Then,the base layer fusion image is generated from the extracted infrared targets and the base layer image of visible light using the base layer fusion rules.Finally,the final fusion image is derived from the base layer fusion image using the detail layer fusion rules.Experimental results evidently demonstrate that the proposed algorithm yields fused images with well-defined targets,distinct textural details,and abundant information.Moreover,the proposed algorithm stands out for its simplicity,efficiency,and broad applicability.Compared with the other four algorithms,the proposed algorithm demonstrates superior performance in both subjective and objective evaluations,andhas certain engineering deployment capability.
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