Abstract: Due to the shortcomings of under enhancement, over enhancement, and poor edge detail enhancement in infrared image enhancement algorithms, this paper proposes an infrared image enhancement algorithm that combines Otsu algorithm and dual region histogram equalization to improve the visual effect of infrared images, highlight image detail information, and avoid the aforementioned shortcomings. Firstly, based on the 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, based on the Otsu algorithm, the infrared image is segmented into foreground and background regions. 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. This article conducts experiments based on the FLIR infrared dataset and compares them with four algorithms: traditional histogram equalization, dual histogram equalization, adaptive histogram equalization with limited contrast, and existing dual histogram equalization based on K-Means algorithm. The experimental results show that the algorithm proposed in this paper has better subjective visual effects, and the average performance indicators of information entropy, average gradient, peak signal-to-noise ratio, and spatial frequency of the two scene images after enhancement have increased by 1.2396, 2.6046, 7.1581, and 6.3042 respectively compared to the original images. Finally, 23 benchmark functions were used to test the performance of the improved cuckoo algorithm in this paper. For benchmark functions with different features, the convergence speed and solution accuracy of the algorithm in this paper were significantly improved. Overall, this algorithm has certain advantages in the field of infrared image enhancement.