Abstract: Due to the shortcomings of under- and over-enhancement and poor enhancement of edge details in infrared image enhancement algorithms, we propose an infrared image enhancement algorithm that combines Otsu's algorithm and dual region histogram equalization to improve the visual effect of infrared images, highlight image detail information, and avoid the abovementioned shortcomings. Firstly, based on a teaching and learning search strategy and the elite reverse learning strategy, the cuckoo algorithm is improved to search for the optimal segmentation threshold of the image. Then, the infrared image is segmented into foreground and background regions based on Otsu's algorithm. Finally, local histogram equalization enhancement is applied to the foreground region, and contrast limited histogram equalization enhancement is used to the background region. The two regions are concatenated to obtain an enhanced image. We conducted experiments based on the FLIR infrared dataset and compared them with four algorithms, including traditional histogram equalization, dual histogram equalization, adaptive histogram equalization with limited contrast, and existing dual histogram equalization based on k-means clustering. The experimental results show that the proposed algorithm has better subjective visual effects, and the average performance indicators of information entropy, average gradient, peak signal-to-noise ratio, and spatial frequency of two images after enhancement increased by 1.2396, 2.6046, 7.1581, and 6.3042 respectively compared to the original images. Finally, 4 benchmark functions were used to test the performance of the improved cuckoo algorithm. For benchmark functions with different features, the convergence speed and solution accuracy of the proposed algorithm were significantly improved. Overall, this algorithm exhibited certain advantages in the field of infrared image enhancement.