Histogram Equalization Based Infrared Image Enhancement with FCOWA-ER

The temperature of the target and the surrounding environment are often close and the detection distance is long, resulting in the contrast between the target and the background is not obvious and the texture information is poor for infrared images. To solve these problems, a histogram equalization method of infrared image fusion based on uncertainty reasoning is proposed in this paper. After noise suppression and texture enhancement, considering the characteristics of different histogram equalization methods, this paper uses Fuzzy-Cautious Ordered Weighted Averaging and Evidential Reasoning (FCOWA-ER) method to jointly use three histogram equalization methods which are Contrast Limited Adaptive Histogram Equalization (CLAHE), Dynamic Quadrants Histogram Equalization Plateau Limit (DQHEPL) and Plateau Histogram Equalization (PHE). Experimental results show that compared with the single histogram equalization method, the proposed evidence theory fusion histogram equalization method has better performance on the infrared dataset.