Abstract: To address the problems of low contrast, Gaussian white noise degradation of image quality, and loss of detail in infrared images collected by traditional infrared detectors, an infrared image target enhancement algorithm integrating multi-scale filtering and image detail enhancement is proposed. The algorithm solves the problems encountered by the traditional Retinex algorithm, such as loss of details and edge information in infrared images caused by Gaussian blur, by improving its blurring method, that is, replacing Gaussian blur with bilateral blur. It also simultaneously enhances the contrast of infrared images. With this approach, halo artifacts that appear owing to excessive contrast enhancement of infrared images are eliminated by multi-scale filtering and denoising processes, such as 3D block-matched and non-local mean filtering. The feasibility of this algorithm was verified through experiments, and comparison experiments showed that it was more effective than others in removing Gaussian white noise from infrared images. These results revealed that the target infrared image obtained by this algorithm had enhanced detail information, good clarity, and high contrast, indicating superior performance over other image enhancement algorithms.