A high-real-time, high-quality infrared image equalization algorithm has been developed to address the frequent brightness or darkness in captured images caused by the image's large dynamic range and high-speed motion of the targets and cameras during target tracking with infrared cameras. The proposed algorithm adjusts the contrast between the image subject and background by optimizing the exposure parameters of the infrared camera. First, the image is partitioned by analyzing brightness variations in adjacent areas. Then, non-continuous area retrieval is performed to identify the image subject, which is weighted to calculate the image brightness. To support camera miniaturization, the interpolation method is combined with the lookup table method to iteratively adjust the exposure parameters based on the image's average brightness. This algorithm ensures image subject clarity, speed of infrared camera exposure adjustment, and eliminates the need for additional storage units. Infrared camera testing demonstrates that the proposed algorithm achieves infrared image equalization at an average speed of 3.2 frames per second with an average exposure error of 5.15%, highlighting its practical application value.