The traditional top-hat method is a commonly used method that quickly separates targets from a background. It is used for its fast processing speed and wide range of applications on programmable hardware. However, in some important fields such as microfluidic control, medicine, aerospace, and optical measurement, the observed targets are often spotted with different sizes. The formation mechanism of multiscale spots varies from each other so that they can not be successfully extracted and classified by the traditional top-hat method. To ensure the integrity of targets with a specific size and suppressed noise, the imaging mechanism of different types of spots are studied, and an improved top-hat method with a gray-scale value-based transform is proposed. Compared with the traditional top-hat method, the proposed algorithm is more effective in completely removing unwanted spots. The calculated results of the simulated and real images verify the effectiveness of the double top-hat method in extracting targets with a specific size. Additionally, the resolution of this method is up to the parameter k, which has been discussed in this paper. Furthermore, a multi-top-hat algorithm is presented to distinguish spots of different sizes, and it could be used for real-time multiscale target detection and tracking, as well as real-time multiscale target detection and tracking.