Abstract: When detecting small targets in LiDAR imaging in interference environments, the accuracy of small target detection results is low due to the gray level collapse and edge contour inundation caused by complex background radiation. Therefore, this study proposes an accurate detection method for small targets based on the dual layer enhancement approach. After capturing the original image, guided filtering is used to decompose the image into a base layer and a detail layer. The basic layer compresses the dynamic range through adaptive guided filtering histogram mapping to correct gray level collapse, while the detail layer uses saturation processing to suppress shot noise and restores submerged edge contours through adaptive gain control. Two layers are fused with fixed coefficient weighting to achieve detail enhancement. Then, the local entropy operator is improved through spatial distance weighting, and multi-scale local contrast is fused to construct a high signal-to-noise ratio feature map to counteract residual local entropy scattering and artifact interference. Finally, using adaptive threshold segmentation to achieve precise separation of small targets from the background. The experiment shows that under interference environment, the edge preservation index of this method is close to or higher than 1, and the clutter suppression factor, signal-to-noise ratio gain, and background suppression factor are significantly improved. Moreover, the regional non-uniformity is always below 0.2, which verifies the robustness of this method in accurate detection of small targets.